-
Lecture Notes in Computer Science 6691Commenced Publication in
1973Founding and Former Series Editors:Gerhard Goos, Juris
Hartmanis, and Jan van Leeuwen
Editorial Board
David HutchisonLancaster University, UK
Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA
Josef KittlerUniversity of Surrey, Guildford, UK
Jon M. KleinbergCornell University, Ithaca, NY, USA
Alfred KobsaUniversity of California, Irvine, CA, USA
Friedemann MatternETH Zurich, Switzerland
John C. MitchellStanford University, CA, USA
Moni NaorWeizmann Institute of Science, Rehovot, Israel
Oscar NierstraszUniversity of Bern, Switzerland
C. Pandu RanganIndian Institute of Technology, Madras, India
Bernhard SteffenTU Dortmund University, Germany
Madhu SudanMicrosoft Research, Cambridge, MA, USA
Demetri TerzopoulosUniversity of California, Los Angeles, CA,
USA
Doug TygarUniversity of California, Berkeley, CA, USA
Gerhard WeikumMax Planck Institute for Informatics,
Saarbruecken, Germany
-
Joan Cabestany Ignacio RojasGonzalo Joya (Eds.)
Advancesin ComputationalIntelligence
11th International Work-Conferenceon Artificial Neural Networks,
IWANN 2011Torremolinos-Málaga, Spain, June 8-10, 2011Proceedings,
Part I
13
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Volume Editors
Joan CabestanyUniversitat Politècnica de Catalunya
(UPC)Departament d’Enginyeria ElectrònicaCampus Nord, Edificio C4,
c/ Gran Capità s/n, 08034 Barcelona, SpainE-mail:
[email protected]
Ignacio RojasUniversity of GranadaDepartment of Computer
Architecture and Computer TechnologyC/ Periodista Daniel Saucedo
Aranda, 18071 Granada, SpainE-mail: [email protected]
Gonzalo JoyaUniversidad de Málaga, Departamento Tecnologia
ElectrónicaCampus de Teatinos, 29071 Málaga, SpainE-mail:
[email protected]
ISSN 0302-9743 e-ISSN 1611-3349ISBN 978-3-642-21500-1 e-ISBN
978-3-642-21501-8DOI 10.1007/978-3-642-21501-8Springer Heidelberg
Dordrecht London New York
Library of Congress Control Number: 2011928243
CR Subject Classification (1998): J.3, I.2, I.5, C.2.4, H.3.4,
D.1, D.2
LNCS Sublibrary: SL 1 – Theoretical Computer Science and General
Issues
© Springer-Verlag Berlin Heidelberg 2011This work is subject to
copyright. All rights are reserved, whether the whole or part of
the material isconcerned, specifically the rights of translation,
reprinting, re-use of illustrations, recitation,
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Printed on acid-free paper
Springer is part of Springer Science+Business Media
(www.springer.com)
-
Preface
We are proud to present the set of final accepted papers for the
eleventh editionof the IWANN conference “International
Work-Conference on Artificial NeuralNetworks” held in Torremolinos
(Spain) during June 8–10, 2011.
IWANN is a biennial conference that seeks to provide a
discussion forumfor scientists, engineers, educators and students
about the latest ideas and real-izations in the foundations,
theory, models and applications of hybrid systemsinspired by nature
(neural networks, fuzzy logic and evolutionary systems) aswell as
in emerging areas related to the above items. As in previous
editionsof IWANN, this year’s event also aimed to create a friendly
environment thatcould lead to the establishment of scientific
collaborations and exchanges amongattendees. Since the first
edition in Granada (LNCS 540, 1991), the conferencehas evolved and
matured. The list of topics in the successive Call for Papers
hasalso evolved, resulting in the following list for the present
edition:
1. Mathematical and theoretical methods in computational
intelli-gence: Mathematics for neural networks; RBF structures;
Self-organizingnetworks and methods; Support vector machines and
kernel methods; Fuzzylogic; Evolutionary and genetic algorithms
2. Neurocomputational formulations: Single-neuron modelling;
Perceptualmodelling; System-level neural modelling; Spiking
neurons; Models of bio-logical learning
3. Learning and adaptation: Adaptive systems; Imitation
learning; Recon-figurable systems; Supervised, non-supervised,
reinforcement and statisticalalgorithms
4. Emulation of cognitive functions: Decision making;
Multi-agent systems;Sensor mesh; Natural language; Pattern
recognition; Perceptual and motorfunctions (visual, auditory,
tactile, virtual reality, etc.); Robotics; Planningmotor
control
5. Bio-inspired systems and neuro-engineering: Embedded
intelligent sys-tems; Evolvable computing; Evolving hardware;
Microelectronics for neural,fuzzy and bioinspired systems; Neural
prostheses; Retinomorphic systems;Brain–computer interfaces (BCI)
nanosystems; Nanocognitive systems
6. Hybrid intelligent systems: Soft computing; Neuro-fuzzy
systems; Neuro-evolutionary systems; Neuro-swarm; Hybridization
with novel computingparadigms: Qantum computing, DNA computing,
membrane computing;Neural dynamic logic and other methods; etc.
7. Applications: Image and signal processing; Ambient
intelligence; Biomimeticapplications; System identification,
process control, and manufacturing; Com-putational biology and
bioinformatics; Internet modeling, communicationand networking;
Intelligent systems in education; Human–robot
interaction.Multi-agent systems; Time series analysis and
prediction; Data mining andknowledge discovery
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VI Preface
At the end of the submission process, we had 202 papers on the
above topics.After a careful peer-review and evaluation process
(each submission was reviewedby at least 2, and on average 2.4,
Program Committee members or additionalreviewer), 154 papers were
accepted for oral or poster presentation, accordingto the
recommendations of reviewers and the authors’ preferences.
It is important to note that for the sake of consistency and
readability ofthe book, the presented papers are not organized as
they were presented in theIWANN 2011 sessions, but classified under
21 chapters and with one chapteron the associated satellite
workshop. The organization of the papers is in twovolumes and
arranged following the topics list included in the call for
papers.The first volume (LNCS 6691), entitled Advances in
Computational Intelligence.Part I is divided into ten main parts
and includes the contributions on:
1. Mathematical and theoretical methods in computational
intelligence2. Learning and adaptation3. Bio-inspired systems and
neuro-engineering4. Hybrid intelligent systems5. Applications of
computational intelligence6. New applications of brain–computer
interfaces7. Optimization algorithms in graphic processing units8.
Computing languages with bio-inspired devices and multi-agent
systems9. Computational intelligence in multimedia processing
10. Biologically plausible spiking neural processing
In the second volume (LNCS 6692), with the same title as the
previous vol-ume, we have included the contributions dealing with
topics of IWANN andalso the contributions to the associated
satellite workshop (ISCIF 2011). Thesecontributions are grouped
into 11 chapters with one chapter on the satelliteworkshop:
1. Video and image processing2. Hybrid artificial neural
networks: models, algorithms and data3. Advances in machine
learning for bioinformatics and computational
biomedicine4. Biometric systems for human–machine interaction5.
Data mining in biomedicine6. Bio-inspired combinatorial
optimization7. Applying evolutionary computation and
nature-inspired algorithms to for-
mal methods8. Recent advances on fuzzy logic and soft computing
applications9. New advances in theory and applications of ICA-based
algorithms
10. Biological and bio-inspired dynamical systems11. Interactive
and cognitive environments12. International Workshop of Intelligent
Systems for Context-Based Informa-
tion Fusion (ISCIF 2011)
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Preface VII
During the present edition, the following associated satellite
workshops wereorganized:
1. 4th International Conference on Computational Intelligence in
Se-curity for Information Systems (CISIS 2011). CISIS aims to offer
ameeting opportunity for academic and industry-related researchers
belongingto the various vast communities of computational
intelligence, informationsecurity, and data mining. The
corresponding selected papers are publishedin an independent volume
(LNCS 6694).
2. International Workshop of Intelligent Systems for
Context-BasedInformation Fusion (ISCIF 2011). This workshop
provides an interna-tional forum to present and discuss the latest
scientific developments andtheir effective applications, to assess
the impact of the approach, and to fa-cilitate technology transfer.
The selected papers are published as a separatechapter in the
second volume (LNCS 6692).
3. Third International Workshop on Ambient-Assisted
Living(IWAAL). IWAAL promotes the collaboration among researchers
in thisarea, concentrating efforts on the quality of life, safety
and health problems ofelderly people at home. IWAAL papers are
published in LNCS volume 6693.
The 11th edition of IWANN was organized by the Universidad de
Malaga,Universidad de Granada and Universitat Politecnica de
Catalunya, together withthe Spanish Chapter of the IEEE
Computational Intelligence Society. We wishto thank to the Spanish
Ministerio de Ciencia e Innovacion and the Universityof Malaga for
their support and grants.
We would also like to express our gratitude to the members of
the differentcommittees for their support, collaboration and good
work. We specially thankthe organizers of the associated satellite
workshops and special session organiz-ers. Finally, we want to
thank Springer, and especially Alfred Hofmann, AnnaKramer and Erika
Siebert-Cole, for their continuous support and cooperation.
June 2011 Joan CabestanyIgnacio RojasGonzalo Joya
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Organization
IWANN 2011 Organizing Committee
Honorary Chairs
Alberto Prieto University of GranadaFrancisco Sandoval
University of Malaga
Conference ChairsJoan Cabestany Polytechnic University of
CataloniaIgnacio Rojas University of GranadaGonzalo Joya University
of Malaga
Technical Program Chairs
Francisco Garcia University of MalagaMiguel Atencia University
of Malaga
Satellite Worshops Chairs
Juan M. Corchado University of SalamancaJose Bravo University of
Castilla la Mancha
Publicity and Publication Chairs
Pedro Castillo University of GranadaAlberto Guillen University
of GranadaBeatriz Prieto University of Granada
IWANN 2011 Program Committee
Plamen Angelov University of LancasterCecilio Angulo Polytechnic
University of CataloniaA. Artes Rodriguez University of Carlos III,
MadridAntonio Bahamonde University of OviedoR. Babuska Delft
University of TechnologySergi Bermejo Polytechnic University of
CataloniaPiero P. Bonissone GE Global ResearchAndreu Catala
Polytechnic University of CataloniaGert Cauwenberghs University of
California, San DiegoJesus Cid-Sueiro University of Carlos III,
MadridRafael Corchuelo University of Seville
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X Organization
Óscar Cordón European Centre for Soft ComputingCarlos Cotta
University of MalagaMarie Cottrell University of Paris IAlicia
D’Anjou University of the Basque CountryLuiza De Macedo Mourelle
State University of Rio de Janeiro (UERJ)Dante Del Corso
Polytechnic of TurinAngel P. del Pobil University of Jaume I,
CastellonRichard Duro University of A CoruñaMarcos Faundez-Zanuy
Polytechnic University of MataroJ. Manuel Ferrández Polytechnic
University of CartagenaKunihiko Fukushima Takatsuki, OsakaChistian
Gamrat CEA, Gif sur YvettePatrik Garda University Paris Sud,
OrsayF. Javier Gonzalez Cañete University of MalagaKarl Goser
University of DortmundManuel Graña University of the Basque
CountryAnne Guerin-Dugue Institut National Polytechnique
de GrenobleHani Hagras University of EssexAlister Hamilton
University of EdinburghJeanny Hérault GIPSA-Lab, INPG,
GrenobleLuis Javier Herrera University of GranadaFrancisco Herrera
University of GranadaCesar Hervás University of CordobaTom Heskes
Radboud University NijmegenPedro Isasi University of Carlos III,
MadridSimon Jones University of LoughbouroughChristian Jutten
GIPSA-lab/DIS - CNRS - Grenoble
UniversityKathryn Klemic Yale UniversityAmaury Lendasse Helsinki
University of TechnologyKurosh Madani University of Paris XIIJordi
Madrenas Polytechnic University of CataloniaLúıs Magdalena ECSC
MieresDario Maravall Polytechnic University of MadridBonifacio
Mart́ın Del Brio University of ZaragozaFrancesco Masulli University
of La Spezia, GenoaJose M. Molina University of Carlos III,
MadridAugusto Montisci University of CagliariClaudio Moraga
European Centre for Soft ComputingJuan M. Moreno Polytechnic
University of CataloniaKlaus-Robert Muller FIRST, BerlinJose Muñoz
University of MalagaAlan F. Murray Edinburgh UniversityJean-Pierre
Nadal Normal Superior School, Paris
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Organization XI
Nadia Nedjah State University of Rio de JaneiroErkki Oja
Helsinki University of TechnologyMadalina Olteanu University of
Paris IJulio Ortega University of GranadaKevin M. Passino The Ohio
State University USAWitold Pedrycz University of AlbertaFrancisco
Pelayo University of GranadaVincenzo Piuri University of
MilanHector Pomares University of GranadaCarlos G. Puntonet
University of GranadaLeonardo Reyneri Polytechnic of TurinEduardo
Ros University of GranadaUlrich Rueckert University of
PaderbornEduardo Sanchez LSI, EPFLJordi Solé-Casals University of
VicPeter Szolgay Pazmany Peter Catholic UniversityJohn Taylor Kings
College London, UKCarme Torras Polytechnic University of
CataloniaI. Burhan Turksen TOBB Econ Technol. University,
AnkaraMark Van Rossum University of EdinburghMarley Vellasco
Pontif. Catholic University of Rio
de JaneiroAlfredo Vellido Polytechnic University of
CataloniaMichel Verleysen Catholic University of
Louvain-la-NeuveThomas Villmann University of LeipzigChangjiu Zhou
Singapore PolytechnicAhmed Zobaa University of CairoPedro Zufiria
Polytechnic University of Madrid
IWANN 2011 Reviewers
Carlos Affonso Nove de Julho UniversityVanessa Aguiar University
of A CoruñaArnulfo Alanis Garza Instituto Tecnologico de
TijuanaAmparo Alonso-Betanzos University of A CoruñaJuan Antonio
Alvarez University of SevilleJhon Edgar Amaya University of
TachiraCésar Andrés Complutense University of MadridAnastassia
Angelopoulou University of WestminsterPlamen Angelov Lancaster
UniversityDavide Anguita University of GenoaCecilio Angulo
Polytechnic University of CataloniaAngelo Arleo CNRS - University
Pierre and Marie Curie
Paris VIManuel Atencia IIIA-CSICMiguel Atencia University of
Malaga
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XII Organization
Jorge Azorin University of AlicanteDavide Bacciu IMT Lucca
School for Advanced StudiesAntonio Bahamonde University of Oviedo
at Gijón, AsturiasHalima Bahi University of AnnabaJavier Bajo
Pont. University of SalamancaJuan Pedro Bandera University of
MalagaCristian Barrué Polytechnic University of CataloniaBruno
Baruque University of BurgosDavid Becerra University of the West of
ScotlandLluis A. Belanche-Munoz Polytechnic University of
CataloniaSergi Bermejo Polytechnic University of CataloniaNicu
Bizdoaca University of CraiovaJuan Botia University of MurciaJulio
Bregáins University of A CoruñaGloria Bueno University of
Castilla-La ManchaJoan Cabestany Polytechnic University of
CataloniaInma P Cabrera University of MalagaTomasa Calvo University
of AlcalaJose Luis Calvo-Rolle University of A CoruñaMariano
Carbonero-Ruz ETEA - Cordoba UniversityCarlos Carrascosa GTI-IA
DSIC Universidad Politecnica
de ValenciaLuis Castedo University of A CoruñaPedro Castillo
University of GranadaAna Cavalli GET/INTMiguel Cazorla University
of AlicanteRaymond Chiong Swinburne University of TechnologyJesus
Cid-Sueiro University of MadridMáximo Cobos Universidad
Politecnica de ValenciaValentina Colla Scuola Superiore S.
AnnaFeijoo Colomine University of TachiraPablo Cordero University
of MalagaÓscar Cordón European Centre for Soft ComputingFrancesco
Corona TKKUlises Cortes Polytechnic University of CataloniaCarlos
Cotta University of MalagaMarie Cottrell Universite Paris IMario
Crespo-Ramos University of OviedoRaúl Cruz-Barbosa Universidad
Tecnológica de la MixtecaManuel Cruz-Ramı́rez Departamento de
Informática y Análisis
NuméricoErzsébet Csuhaj-Varjú Hungarian Academy of
SciencesDaniela Danciu University of CraiovaAdriana Dapena
University of A CoruñaAlberto De La Encina Universidad
Complutense
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Organization XIII
Luiza De Macedo Mourelle State University of Rio de Janeiro
(UERJ)Suash Deb C.V. Raman College of EngineeringJosé Del
Campo-Ávila University of MalagaAngel P. Del Pobil Jaume-I
UniversityEnrique Dominguez University of MalagaJulian Dorado
University of A CoruñaRichard Duro University of A CoruñaGregorio
Dı́az University of Castilla-La ManchaMarta Dı́az Polytechnic
University of CataloniaEmil Eirola Helsinki University of
TechnologyPatrik Eklund Umea UniversityPablo Estevez University of
ChileMarcos Faundez-Zanuy Escola Universitaria Politecnica de
MataroCarlos Fernandez University of A CoruñaJ. Fernandez De
Cañete University of MalagaAlberto Fernandez Gil University Rey
Juan CarlosE. Fernandez-Blanco University of A CoruñaJ.C.
Fernández Caballero University of CordobaM. Fernández Carmona
University of MalagaF. Fernández De Vega University of
ExtremaduraAntonio Fernández Leiva University of MalagaF.
Fernández Navarro University of CordobaJ. Manuel Ferrández
Universidad Politecnica de CartagenaAnibal R. Figueiras-Vidal
Universidad Politecnica de MadridOscar Fontenla-Romero University
of A CoruñaLeonardo Franco University of MalagaAna Freire
University of A CoruñaRamón Fuentes Universidad Publica de
NavarraColin Fyfe University of the west of scotlandJosé Gallardo
University of MalagaJose Garcia Rodŕıguez University of
AlicanteFrancisco Garcia-Lagos University of MalagaMaite
Garcia-Sebastian University of the Basque CountryJuan Miguel
Garćıa Universidad Politecnica de ValenciaPatricio Garćıa Báez
University of La LagunaPablo Garćıa Sánchez University of
GranadaMaribel Garćıa-Arenas University of GranadaEsther
Garćıa-Garaluz University of MalagaPatrick Garda UPMC
(France)Marcos Gestal University of A CoruñaPeter Gloesekotter
University of Applied Sciences MünsterJuan Gomez University of
MadridLuis González Abril University of SevilleJesús González
Peñalver University of GranadaJuan Gorriz University of
Granada
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XIV Organization
Karl Goser University of DortmundBernard Gosselin Université de
MonsJorge Gosálbez Universidad Politecnica de ValenciaManuel Grana
University of the Basque CountryBertha Guijarro-Berdiñas
University of A CoruñaNicolás Guil University of MalagaAlberto
Guillen University of GranadaPedro Antonio Gutiérrez University of
CordobaVanessa Gómez-Verdejo University of MadridAndrei Halanay
Polytechnic University of BucharestAlister Hamilton University of
EdinburghFrancisco Herrera University of GranadaÁlvaro Herrero
University of BurgosCesar Hervás University of CordobaTom Heskes
Radboud University NijmegenM. Hidalgo-Herrero Universidad
ComplutenseRob Hierons Brunel UniversityWei-Chiang Hong School of
Management, Da Yeh UniversityJeanny Hérault GIPSA-Lab, INPG,
GrenobleJosé Jerez University of MalagaM.D. Jimenez-Lopez
University of Rovira i VirgiliJ.L. Jiménez Laredo University of
GranadaSimon Jones University of LoughbouroughGonzalo Joya
University of MalagaVicente Julian GTI-IA DSIC UPVChristian Jutten
GIPSA-lab/DIS - CNRS - Grenoble
UniversityJorma Laaksonen Helsinki University of
TechnologyAlberto Labarga University of GranadaVincent Lemaire
Orange LabsAmaury Lendasse HUTPaulo Lisboa Liverpool John Moores
UniversityEzequiel Lopez University of MalagaRafael Luque
University of MalagaOtoniel López Miguel Hernandez
UniversityGuillermo López Campos Institute of Health “Carlos
III”M.A. López Gordo University of GranadaKurosh Madani LISSI /
Université PARIS XIIJordi Madrenas Polytechnic University of
CataloniaLúıs Magdalena ECSC MieresEnric Xavier Martin Rull
Polytechnic University of CataloniaLuis Mart́ı University of
MadridMario Mart́ın Polytechnic University of CataloniaBonifacio
Mart́ın Del Brio University of ZaragozaJosé Mart́ın Guerrero
Universiy of Valencia
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Organization XV
José Lúıs Mart́ınez University of Castilla-La ManchaF.J.
Mart́ınez-Estudillo ETEAFrancesco Masulli University of
GenovaMontserrat Mateos Pont. University of SalamancaJesús
Medina-Moreno University of CadizMercedes Merayo Complutense
University of MadridJuan J. Merelo University of GranadaGustavo J.
Meschino National University of Mar del PlataJose M. Molina
University of MadridCarlos Molinero Complutense University of
MadridFederico Montesini-Pouzols HUTAugusto Montisci University of
CagliariAntonio Mora University of GranadaAngel Mora Bonilla
University of MalagaClaudio Moraga European Centre for Soft
ComputingGin Moreno University of Castilla la ManchaJuan M. Moreno
Polytechnic University of CataloniaJuan Moreno Garćıa University
of Castilla-La ManchaJose Muñoz University of MalagaSusana Muñoz
Hernández Technical University of MadridE. Mérida-Casermeiro
University of MalagaNadia Nedjah State University of Rio de
JaneiroPedro Nuñez University of ExtremaduraManuel Núñez
UCMSalomon Oak California State Polytechnic UniversityManuel
Ojeda-Aciego University of MalagaMadalina Olteanu SAMOS,
Université Paris 1Jozef Oravec PF UPJSJulio Ortega University of
GranadaA. Ortega De La Puente Autonomous University of MadridJuan
Miguel Ortiz University of MalagaInma P. De Guzmán University of
MalagaOsvaldo Pacheco Universidade de AveiroEsteban Palomo
University of MalagaDiego Pardo Polytechnic University of
CataloniaMiguel Angel Patricio University of de MadridFernando L.
Pelayo University of Castilla-La ManchaFrancisco Pelayo University
of GranadaVincenzo Piuri University of MilanHector Pomares
University of GranadaAlberto Prieto University of GranadaMar Prueba
University of MalagaAleka Psarrou University of
WestminsterFrancisco Pujol University of AlicanteCarlos G. Puntonet
University of Granada
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XVI Organization
José Manuel Pérez University of JaenPablo Rabanal Complutense
University of MadridJuan Rabuñal University of A CoruñaAnder
Ramos University of TübingenDaniel Rivero University of A
CoruñaIsmael Rodriguez Laguna Complutense University of MadridA.
Rodriguez-Molinero Hospital Sant Antoni AbatJuan Antonio Rodŕıguez
University of MalagaSara Rodŕıguez University of SalamancaDavid
Rodŕıguez Rueda University of TachiraIgnacio Rojas University of
GranadaFernando Rojas University of GranadaEnrique Romero
Polytechnic University of CataloniaSamuel Romero Garcia University
of GranadaRicardo Ron University of MalagaEduardo Ros University of
GranadaFabrice Rossi TELECOM ParisTechPeter Roth Graz University of
TechnologyLeonardo Rubio University of GranadaFernando Rubio Dı́ez
Complutense University of MadridUlrich Rueckert University of
PaderbornNicolás Ruiz Reyes University of JaenAmparo Ruiz
Sepúlveda University of MalagaJoseph Rynkiewicz University of
Paris IVladimir Râsvan University of CraiovaAddisson Salazar
Universidad Politecnica de ValenciaSancho Salcedo-Sanz University
of AlcaláAlbert Samà Polytechnic University of CataloniaMiguel A.
Sanchez Pontifical University of SalamancaFrancisco Sandoval
University of MalagaJose Santos University of A CoruñaJ.A. Seoane
Fernández University of A CoruñaEduardo Serrano Autonomous
University of MadridOlli Simula Helsinki University of
TechnologyEvgeny Skvortsov Simon Fraser UniversitySergio Solinas
Università degli studi di PaviaJordi Solé-Casals Universitat de
VicAdrian Stoica Polytechnic University of BucharestJosé Luis
Subirats University of MalagaPeter Szolgay Pazmany Peter Catholic
UniversityJavier Sánchez-Monedero University of CordobaAna Maria
Tomé Universidade de AveiroCarme Torras Polytechnic University of
CataloniaClaude Touzet Université de ProvenceGracián Triviño
University of Malaga
-
Organization XVII
Ricardo Téllez Pal RoboticsRaquel Ureña University of
GranadaOlga Valenzuela University of GranadaGermano Vallesi
Università Politecnica delle
Marche - AnconaAgust́ın Valverde University of MalagaPablo
Varona Autonomous University of MadridM.A. Veganzones University of
the Basque CountrySergio Velast́ın Kingston UniversityMarley
Vellasco PUC-RioAlfredo Vellido Polytechnic University of
CataloniaFrancisco Veredas University of MalagaMichel Verleysen
Université catholique de LouvainBart Wyns Ghent UniversityVicente
Zarzoso University of Nice Sophia AntipolisCarolina Zato University
of SalamancaAhmed Zobaa University of Exeter
IWANN 2011 Invited Speakers
Hani Hagras The Computational Intelligence CentreSchool of
Computer Science andElectronic Engineering, University of
Essex,UK
Francisco Herrera Head of Research Group SCI2S(Soft Computing
and Intelligent InformationSystems), Department of Computer
Scienceand Artificial Intelligence,University of Granada, Spain
Tom Heskes Head of Machine Learning Group,Intelligent Systems
Institute for Computingand Information Sciences (iCIS) Faculty
ofScience Radboud University Nijmegen,The Netherlands
IWANN 2011 Special Sessions Organizers
New Applications of Brain–Computer Interfaces
Francisco Pelayo University of GranadaM.A. López Gordo
University of GranadaRicardo Ron University of Malaga
-
XVIII Organization
Optimization Algorithms in Graphic Processing Units
Antonio Mora University of GranadaMaribel Garćıa-Arenas
University of GranadaPedro Castillo University of Granada
Computing Languages with Bio-inspired Devices
M. D. Jimenez-Lopez University of Rovira i VirgiliA. Ortega De
La Puente Autonomous University of Madrid
Computational Intelligence in Multimedia
Adriana Dapena University of A CoruñaJulio Bregáins University
of A CoruñaNicolás Guil University of Malaga
Biologically Plausible Spiking Neural Processing
Eduardo Ros University of GranadaRichard R. Carrillo University
of Almeria
Video and Image Processing
Enrique Domı́nguez University of MalagaJosé Garćıa University
of Alicante
Hybrid Artificial Neural Networks: Models, Algorithms and
Data
Cesar Hervás University of CordobaPedro Antonio Gutiérrez
University of Crdoba
Advances in Machine Learning for Bioinformatics and
ComputationalBiomedicinePaulo J.L. Lisboa Liverpool John Moores
UniversityAlfredo Vellido Polytechnic University of
CataloniaLeonardo Franco University of Malaga
Biometric Systems for Human–Machine Interaction
Alexandra Psarrou University of WestminsterAnastassia
Angelopoulou University of WestminsterC.M. Travieso-Gonzlez
University of Las Palmas de Gran CanariaJordi Solé-Casals
University of Vic
-
Organization XIX
Data Mining in Biomedicine
Julián Dorado University of A CoruñaJuan R. Rabuñal
University of A CoruñaAlejandro Pazos University of A Coruña
Bio-inspired Combinatorial Optimization
Carlos Cotta Porras University of MalagaAntonio J. Fernández
Leiva University of Malaga
Applying Evolutionary Computation and Nature-InspiredAlgorithms
to Formal Methods
Ismael Rodŕıguez Complutense University of Madrid
Recent Advances on Fuzzy Logic and Soft Computing
Applications
Inma P. Cabrera University of MalagaPablo Cordero University of
MalagaManuel Ojeda-Aciego University of Malaga
New Advances in Theory and Applications of ICA-Based
Algorithms
Addison Salazar Polytechnic University of ValenciaLuis Vergara
Polytechnic University of Valencia
Biological and Bio-inspired Dynamical Systems
Vladimir Rasvan University of CraiovaDaniela Danciu University
of Craiova
Interactive and Cognitive Environments
Andreu Catalá Polytechnic University of CataloniaCecilio Angulo
Polytechnic University of Catalonia
-
Table of Contents – Part I
Mathematical and Theoretical Methods inComputational
Intelligence
Gaze Gesture Recognition with Hierarchical Temporal
MemoryNetworks . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
David Rozado, Francisco B. Rodriguez, and Pablo Varona
Feature Selection for Multi-label Classification Problems . . .
. . . . . . . . . . . 9Gauthier Doquire and Michel Verleysen
A Novel Grouping Heuristic Algorithm for the Switch
LocationProblem Based on a Hybrid Dual Harmony Search Technique . .
. . . . . . . . 17
Sergio Gil-Lopez, Itziar Landa-Torres, Javier Del Ser,Sancho
Salcedo-Sanz, Diana Manjarres, andJose A. Portilla-Figueras
Optimal Evolutionary Wind Turbine Placement in Wind
FarmsConsidering New Models of Shape, Orography and Wind
SpeedSimulation . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
B. Saavedra-Moreno, S. Salcedo-Sanz, A. Paniagua-Tineo,J.
Gascón-Moreno, and J.A. Portilla-Figueras
Multi-Valued Neurons: Hebbian and Error-Correction Learning . .
. . . . . . 33Igor Aizenberg
Multi-label Testing for CO2RBFN: A First Approach to the
ProblemTransformation Methodology for Multi-label Classification .
. . . . . . . . . . . 41
A.J. Rivera, F. Charte, M.D. Pérez-Godoy, and Maŕıa Jose del
Jesus
Single Neuron Transient Activity Detection by Means ofTomography
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 49
Carlos Aguirre, Pedro Pascual, Doris Campos, and Eduardo
Serrano
Estimate of a Probability Density Function through Neural
Networks . . . 57Leonardo Reyneri, Valentina Colla, and Marco
Vannucci
Learning and Adaptation
A Neural Fuzzy Inference Based Adaptive Controller Using
LearningProcess for Nonholonomic Robots . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 65
Ting Wang, Fabien Gautero, Christophe Sabourin, andKurosh
Madani
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XXII Table of Contents – Part I
A Multi-objective Evolutionary Algorithm for Network
IntrusionDetection Systems . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 73
J. Gómez, C. Gil, R. Baños, A.L. Márquez, F.G. Montoya,
andM.G. Montoya
A Cognitive Approach for Robots’ Vision Using Unsupervised
Learningand Visual Saliency . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 81
Dominik M. Ramı́k, Christophe Sabourin, and Kurosh Madani
Fusing Heterogeneous Data Sources Considering a Set of
EquivalenceConstraints . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
Manuel Mart́ın-Merino
A Novel Heuristic for Building Reduced-Set SVMs Using
theSelf-Organizing Map . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 97
Ajalmar R. Rocha Neto and Guilherme A. Barreto
An Additive Decision Rules Classifier for Network
IntrusionDetection . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
105
Tommaso Pani and Francisco de Toro
Multi-modal Opponent Behaviour Prognosis in E-Negotiations . . .
. . . . . . 113Ioannis Papaioannou, Ioanna Roussaki, and Miltiades
Anagnostou
Bio-inspired Systems and Neuro-engineering
An AER to CAN Bridge for Spike-Based Robot Control . . . . . . .
. . . . . . . 124M. Dominguez-Morales, A. Jimenez-Fernandez, R.
Paz,A. Linares-Barranco, D. Cascado, J.L. Coronado, J.L. Muñoz,
andG. Jimenez
Neuromorphic Real-Time Objects Tracking Using Address
EventRepresentation and Silicon Retina . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 133
F. Gómez- Rodŕıguez, L. Miró-Amarante, M. Rivas, G. Jimenez,
andF. Diaz-del-Rio
Performance Study of Software AER-Based Convolutions on a
ParallelSupercomputer . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Rafael J. Montero-Gonzalez, Arturo Morgado-Estevez,Alejandro
Linares-Barranco, Bernabe Linares-Barranco,Fernando Perez-Peña,
Jose Antonio Perez-Carrasco, andAngel Jimenez-Fernandez
Frequency Analysis of a 64x64 Pixel Retinomorphic System with
AEROutput to Estimate the Limits to Apply onto Specific
MechanicalEnvironment . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Fernando Perez-Peña, Arturo Morgado-Estevez,Alejandro
Linares-Barranco, Gabriel Jimenez-Moreno,Jose Maria
Rodriguez-Corral, and Rafael J. Montero-Gonzalez
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Table of Contents – Part I XXIII
An AER Spike-Processing Filter Simulator and Automatic
VHDLGenerator Based on Cellular Automata . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 157
Manuel Rivas-Perez, A. Linares-Barranco,Francisco
Gomez-Rodriguez, A. Morgado, A. Civit, andG. Jimenez
A Biologically Inspired Neural Network for Autonomous
UnderwaterVehicles . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
166
Francisco Garćıa-Córdova and Antonio Guerrero-González
Hybrid Intelligent Systems
A Preliminary Study on the Use of Fuzzy Rough Set Based
FeatureSelection for Improving Evolutionary Instance Selection
Algorithms . . . . 174
Joaqúın Derrac, Chris Cornelis, Salvador Garćıa, andFrancisco
Herrera
Forecasting Based on Short Time Series Using ANNs and Grey
Theory– Some Basic Comparisons . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 183
Jelena Milojković, Vančo Litovski, Octavio Nieto-Taladriz,
andSlobodan Bojanić
Short-Term Wind Power Forecast Based on Cluster Analysis
andArtificial Neural Networks . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 191
Javier Lorenzo, Juan Méndez, Modesto Castrillón, andDaniel
Hernández
Back Propagation with Balanced MSE Cost Function and
NearestNeighbor Editing for Handling Class Overlap and Class
Imbalance . . . . . 199
R. Alejo, J.M. Sotoca, V. Garćıa, and R.M. Valdovinos
Combination of GA and ANN to High Accuracy of Polarimetric
SARData Classification . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 207
Ataollah Haddadi G. and Mahmodreza Sahebi
Gradient Descent Optimization for Routing in
MultistageInterconnection Networks . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 215
Mehran Ghaziasgar and Armin Tavakoli Naeini
The Command Control of a Two-Degree-of-Freedom Platform by
HandGesture Moment Invariants . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 223
Chih-Lyang Hwang and Chen-Han Yang
Network Intrusion Prevention by Using Hierarchical
Self-OrganizingMaps and Probability-Based Labeling . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 232
Andres Ortiz, Julio Ortega, Antonio F. Dı́az, and Alberto
Prieto
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XXIV Table of Contents – Part I
Applications of Computational Intelligence
Human/Robot Interface for Voice Teleoperation of a
RoboticPlatform . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
L. Gallardo-Estrella and A. Poncela
Graph Laplacian for Semi-supervised Feature Selection in
RegressionProblems . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
248
Gauthier Doquire and Michel Verleysen
Detection of Transients in Steel Casting through Standard
andAI-Based Techniques . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 256
Valentina Colla, Marco Vannucci, Nicola Matarese,Gerard
Stephens, Marco Pianezzola, Izaskun Alonso,Torsten Lamp, Juan
Palacios, and Siegfried Schiewe
Oesophageal Voice Harmonic to Noise Ratio Enhancement over
UMTSNetworks Using Kalman-EM . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 265
Marouen Azzouz, Begoña Garćıa Zapirain, Ibon Ruiz, andAmaia
Méndez
Study of Various Neural Networks to Improve the Defuzzification
ofFuzzy Clustering Algorithms for ROIs Detection in Lung CTs . . .
. . . . . . 273
Alberto Rey, Alfonso Castro, and Bernardino Arcay
Differential Evolution Optimization of 3D Topological
ActiveVolumes . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
J. Novo, J. Santos, and M.G. Penedo
Genetic Algorithms Applied to the Design of 3D Photonic Crystals
. . . . . 291Agust́ın Morgado-León, Alejandro Escúın, Elisa
Guerrero,Andrés Yáñez, Pedro L. Galindo, and Lorenzo Sanchis
Sliding Empirical Mode Decomposition for On-line Analysis
ofBiomedical Time Series . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 299
A. Zeiler, R. Faltermeier, A.M. Tomé, C. Puntonet,A. Brawanski,
and E.W. Lang
Suitability of Artificial Neural Networks for Designing LoC
Circuits . . . . 307David Moreno, Sandra Gómez, and Juan
Castellanos
Aeration Control and Parameter Soft Estimation for a
WastewaterTreatment Plant Using a Neurogenetic Design . . . . . . .
. . . . . . . . . . . . . . . . 315
Javier Fernandez de Canete, Pablo del Saz-Orozco, andInmaculada
Garcia-Moral
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Table of Contents – Part I XXV
Pulse Component Modification Detection in Spino Cerebellar
Ataxia 2Using ICA . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
Rodolfo V. Garćıa, Fernando Rojas, Jesús González, Luis
Velázquez,Roberto Rodŕıguez, Roberto Becerra, and Olga
Valenzuela
Early Pigmentary Retinosis Diagnostic Based on Classification
Trees . . . 329Vivian Sistachs Vega, Gonzalo Joya Caparrós,
andMiguel A. Dı́az Mart́ınez
New Applications of Brain-Computer Interfaces
Audio-Cued SMR Brain-Computer Interface to Drive a
VirtualWheelchair . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
Francisco Velasco-Álvarez, Ricardo Ron-Angevin,Leandro da
Silva-Sauer, Salvador Sancha-Ros, andMaŕıa José Blanca-Mena
A Domotic Control System Using Brain-Computer Interface (BCI) .
. . . . 345Rebeca Corralejo, Roberto Hornero, and Daniel
Álvarez
A Dictionary-Driven SSVEP Speller with a Modified Graphical
UserInterface . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353
Ivan Volosyak, Anton Moor, and Axel Gräser
Non-invasive Brain-Computer Interfaces: Enhanced Gaming
andRobotic Control . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 362
Reinhold Scherer, Elisabeth C.V. Friedrich, Brendan
Allison,Markus Pröll, Mike Chung, Willy Cheung, Rajesh P.N. Rao,
andChrista Neuper
An EEG-Based Design for the Online Detection of
MovementIntention . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
370
Jaime Ibáñez, J. Ignacio Serrano, M. Dolores del Castillo,Luis
Barrios, Juan Álvaro Gallego, and Eduardo Rocon
Auditory Brain-Computer Interfaces for Complete Locked-In
Patients . . 378M.A. Lopez-Gordo, Ricardo Ron-Angevin, andFrancisco
Pelayo Valle
Brain-Computer Interface: Generic Control Interface for
SocialInteraction Applications . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 386
C. Hintermüller, C. Guger, and G. Edlinger
Optimization Algorithms in Graphic Processing Units
Variable Selection in a GPU Cluster Using Delta Test . . . . . .
. . . . . . . . . . 393A. Guillén, M. van Heeswijk, D. Sovilj,
M.G. Arenas, L.J. Herrera,H. Pomares, and I. Rojas
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XXVI Table of Contents – Part I
Towards ParadisEO-MO-GPU: A Framework for GPU-Based LocalSearch
Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 401
N. Melab, T.-V. Luong, K. Boufaras, and E.-G. Talbi
Efficient Simulation of Spatio–temporal Dynamics in
UltrasonicResonators . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
409
Pedro Alonso–Jordá, Jesús Peinado–Pinilla,Isabel
Pérez–Arjona, and Victor J. Sánchez–Morcillo
GPU Implementation of a Bio-inspired Vision Model . . . . . . .
. . . . . . . . . . 417Raquel Ureña, Christian Morillas, Samuel
Romero, andFrancisco J. Pelayo
Bipartite Graph Matching on GPU over Complete or Local
GridNeighborhoods . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 425
Cristina Nader Vasconcelos and Bodo Rosenhahn
GPU Computation in Bioinspired Algorithms: A Review . . . . . .
. . . . . . . . 433M.G. Arenas, A.M. Mora, G. Romero, and P.A.
Castillo
Computing Languages with Bio-inspired Devices andMulti-Agent
Systems
About Complete Obligatory Hybrid Networks of
EvolutionaryProcessors without Substitution . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 441
Artiom Alhazov, Gemma Bel-Enguix, Alexander Krassovitskiy,
andYurii Rogozhin
Chemical Signaling as a Useful Metaphor for Resource Management
. . . . 449Evgeny Skvortsov, Nima Kaviani, and Veronica Dahl
Distributed Simulation of P Systems by Means of Map-Reduce:
FirstSteps with Hadoop and P-Lingua . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 457
L. Diez Dolinski, R. Núñez Hervás, M. Cruz Echeand́ıa, andA.
Ortega
Hierarchy Results on Stateless Multicounter 5′ → 3′
Watson-CrickAutomata . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
465
Benedek Nagy, László Hegedüs, and Ömer Eğecioğlu
Towards a Bio-computational Model of Natural Language Learning .
. . . 473Leonor Becerra-Bonache
Computing Languages with Bio-inspired Devices and
Multi-AgentSystems . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
481
M. Dolores Jiménez-López
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Table of Contents – Part I XXVII
Computational Intelligence in Multimedia Processing
A Novel Strategy for Improving the Quality of Embedded
ZerotreeWavelet Images Transmitted over Alamouti Coding Systems . .
. . . . . . . . . 489
Josmary Labrador, Paula M. Castro, Héctor J. Pérez–Iglesias,
andAdriana Dapena
Applying Data Mining Techniques in a Wyner-Ziv to H.264
VideoTranscoder . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 497
José Luis Mart́ınez, Alberto Corrales-Garćıa, Pedro Cuenca,
andFrancisco José Quiles
On the Use of Genetic Algorithms to Improve Wavelet Sign
CodingPerformance . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 505
Ricardo Garćıa, Otoniel López, Antonio Mart́ı, andManuel P.
Malumbres
Kernel-Based Object Tracking Using a Simple Fuzzy ColorHistogram
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 513
Juan Villalba Espinosa, José Maŕıa González Linares,Julián
Ramos Cózar, and Nicolás Guil Mata
Computational Intelligence in Multimedia Processing . . . . . .
. . . . . . . . . . . 520Nicolás Guil, Julio C. Bregáins, and
Adriana Dapena
Biologically Plausible Spiking Neural Processing
Isometric Coding of Spiking Haptic Signals by Peripheral
SomatosensoryNeurons . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
528
Romain Brasselet, Roland S. Johansson, and Angelo Arleo
Context Separability Mediated by the Granular Layer in a
SpikingCerebellum Model for Robot Control . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 537
Niceto R. Luque, Jesús A. Garrido, Richard R. Carrillo,
andEduardo Ros
Realistic Modeling of Large-Scale Networks: Spatio-temporal
Dynamicsand Long-Term Synaptic Plasticity in the Cerebellum . . . .
. . . . . . . . . . . . 547
Egidio D’Angelo and Sergio Solinas
Event and Time Driven Hybrid Simulation of Spiking
NeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554
Jesus A. Garrido, Richard R. Carrillo, Niceto R. Luque,
andEduardo Ros
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 563
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Table of Contents – Part II
Video and Image Processing
Lossy Image Compression Using a GHSOM . . . . . . . . . . . . .
. . . . . . . . . . . . 1E.J. Palomo, E. Domı́nguez, R.M. Luque,
and J. Muñoz
Visual Features Extraction Based Egomotion Calculation from
aInfrared Time-of-Flight Camera . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 9
Diego Viejo, Jose Garcia, and Miguel Cazorla
Feature Weighting in Competitive Learning for Multiple
ObjectTracking in Video Sequences . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 17
R.M. Luque, J.M. Ortiz-de-Lazcano-Lobato, Ezequiel
López-Rubio,E. Domı́nguez, and E.J. Palomo
The Segmentation of Different Skin Colors Using the Combination
ofGraph Cuts and Probability Neural Network . . . . . . . . . . . .
. . . . . . . . . . . . 25
Chih-Lyang Hwang and Kai-Di Lu
Reduction of JPEG Compression Artifacts by Kernel Regression
andProbabilistic Self-Organizing Maps . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 34
Maŕıa Nieves Florent́ın-Núñez, Ezequiel López-Rubio,
andFrancisco Javier López-Rubio
An Unsupervised Method for Active Region Extraction in
SportsVideos . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
Markos Mentzelopoulos, Alexandra Psarrou, andAnastassia
Angelopoulou
6DoF Egomotion Computing Using 3D GNG-Based Reconstruction . . .
. 50Diego Viejo, Jose Garcia, and Miguel Cazorla
Fast Image Representation with GPU-Based Growing Neural Gas . .
. . . . 58José Garćıa-Rodŕıguez, Anastassia Angelopoulou,
Vicente Morell,Sergio Orts, Alexandra Psarrou, and Juan Manuel
Garćıa-Chamizo
Texture and Color Analysis for the Automatic Classification of
the EyeLipid Layer . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
L. Ramos, M. Penas, B. Remeseiro, A. Mosquera, N. Barreira,
andE. Yebra-Pimentel
Quantitative Study and Monitoring of the Growth of Lung
CancerNodule Using an X-Ray Computed Tomography Image
ProcessingTool . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
José Luis Garćıa Arroyo, Begoña Garćıa Zapirain, andAmaia
Méndez Zorrilla
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XXX Table of Contents – Part II
A Geometrical Method of Diffuse and Specular Image
ComponentsSeparation . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
Ramón Moreno, Manuel Graña, and Alicia d’Anjou
Optical Flow Reliability Model Approximated with RBF . . . . . .
. . . . . . . . 90Agis Rodrigo, Dı́az Javier, Ortigosa Pilar,
Guzmán Pablo, andRos Eduardo
Video and Image Processing with Self-organizing Neural Networks
. . . . . 98José Garćıa-Rodŕıguez, Enrique Domı́nguez,Anastassia
Angelopoulou, Alexandra Psarrou,Francisco José Mora-Gimeno, Sergio
Orts, andJuan Manuel Garćıa-Chamizo
Hybrid Artificial Neural Networks: Models,Algorithms and
Data
Parallelism in Binary Hopfield Networks . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 105José Muñoz-Pérez, Amparo
Ruiz-Sepúlveda, andRafaela Beńıtez-Rochel
Multi-parametric Gaussian Kernel Function Optimization for
�-SVMrUsing a Genetic Algorithm . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 113
J. Gascón-Moreno, E.G. Ortiz-Garćıa, S. Salcedo-Sanz,A.
Paniagua-Tineo, B. Saavedra-Moreno, and J.A. Portilla-Figueras
Face Recognition System in a Dynamical Environment . . . . . . .
. . . . . . . . . 121Aldo Franco Dragoni, Germano Vallesi, and
Paola Baldassarri
Memetic Pareto Differential Evolutionary Neural Network
forDonor-Recipient Matching in Liver Transplantation . . . . . . .
. . . . . . . . . . . 129
M. Cruz-Ramı́rez, C. Hervás-Mart́ınez, P.A. Gutiérrez,J.
Briceño, and M. de la Mata
Studying the Hybridization of Artificial Neural Networks in
HECIC . . . . 137José del Campo-Ávila, Gonzalo
Ramos-Jiménez,Jesús Pérez-Garćıa, and Rafael Morales-Bueno
Processing Acyclic Data Structures Using Modified
Self-OrganizingMaps . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 145
Gabriela Andrejková and Jozef Oravec
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Table of Contents – Part II XXXI
On the Performance of the μ-GA Extreme Learning Machines
inRegression Problems . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 153
A. Paniagua-Tineo, S. Salcedo-Sanz, E.G. Ortiz-Garćıa,J.
Gascón-Moreno, B. Saavedra-Moreno, and J.A. Portilla-Figueras
A Hybrid Evolutionary Approach to Obtain Better
QualityClassifiers . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
161
David Becerra-Alonso, Mariano Carbonero-Ruz,Francisco José
Mart́ınez-Estudillo, andAlfonso Carlos Mart́ınez-Estudillo
Neural Network Ensembles with Missing Data Processing and
DataFusion Capacities: Applications in Medicine and in the
Environment . . . 169
Patricio Garćıa Báez, Carmen Paz Suárez Araujo, andPablo
Fernández López
Hybrid Artificial Neural Networks: Models, Algorithms and Data .
. . . . . 177P.A. Gutiérrez and C. Hervás-Mart́ınez
Advances in Machine Learning for Bioinformaticsand Computational
Biomedicine
Automatic Recognition of Daily Living Activities Based on
aHierarchical Classifier . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 185
Oresti Banos, Miguel Damas, Hector Pomares, and Ignacio
Rojas
Prediction of Functional Associations between Proteins by Means
of aCost-Sensitive Artificial Neural Network . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 194
J.P. Florido, H. Pomares, I. Rojas, J.M. Urquiza, and F.
Ortuño
Hybrid (Generalization-Correlation) Method for Feature Selection
inHigh Dimensional DNA Microarray Prediction Problems . . . . . . .
. . . . . . . 202
Yasel Couce, Leonardo Franco, Daniel Urda, José L. Subirats,
andJosé M. Jerez
Model Selection with PLANN-CR-ARD . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 210Corneliu T.C. Arsene, Paulo J.
Lisboa, and Elia Biganzoli
Biometric Systems for Human-Machine Interaction
Gender Recognition Using PCA and DCT of Face Images . . . . . .
. . . . . . . 220Ondrej Smirg, Jan Mikulka, Marcos
Faundez-Zanuy,Marco Grassi, and Jiri Mekyska
Efficient Face Recognition Fusing Dynamic Morphological
QuotientImage with Local Binary Pattern . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 228
Hong Pan, Siyu Xia, Lizuo Jin, and Liangzheng Xia
-
XXXII Table of Contents – Part II
A Growing Neural Gas Algorithm with Applications in Hand
Modellingand Tracking . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
Anastassia Angelopoulou, Alexandra Psarrou, andJosé Garćıa
Rodŕıguez
Object Representation with Self-Organising Networks . . . . . .
. . . . . . . . . . 244Anastassia Angelopoulou, Alexandra Psarrou,
andJosé Garćıa Rodŕıguez
Data Mining in Biomedicine
SNP-Schizo: A Web Tool for Schizophrenia SNP
SequenceClassification . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252
Vanessa Aguiar-Pulido, José A. Seoane, Cristian R. Munteanu,
andAlejandro Pazos
MicroRNA Microarray Data Analysis in Colon Cancer: Effects
ofNormalization . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 260
Guillermo H. López-Campos, Alejandro Romera-López,Fernando
Mart́ın-Sánchez, Eduardo Diaz-Rubio,Victoria López-Alomso, and
Beatriz Pérez-Villamil
Automatic Handling of Tissue Microarray Cores in
High-DimensionalMicroscopy Images . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
G. Bueno, M. Fernández, O. Déniz, and M. Garćıa-Rojo
Visual Mining of Epidemic Networks . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 276Stéphan Clémençon, Hector De
Arazoza, Fabrice Rossi, andViet-Chi Tran
Bio-inspired Combinatorial Optimization
Towards User-Centric Memetic Algorithms: Experiences with theTSP
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 284
Ana Reyes Badillo, Carlos Cotta, and Antonio J.
Fernández-Leiva
A Multi-objective Approach for the 2D Guillotine Cutting
StockProblem . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
Jesica de Armas, Gara Miranda, and Coromoto León
Ant Colony Optimization for Water Distribution Network Design:A
Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 300
C. Gil, R. Baños, J. Ortega, A.L. Márquez, A. Fernández,
andM.G. Montoya
A Preliminary Analysis and Simulation of Load Balancing
TechniquesApplied to Parallel Genetic Programming . . . . . . . . .
. . . . . . . . . . . . . . . . . . 308
F. Fernández de Vega, J.G. Abengózar Sánchez, and C.
Cotta
-
Table of Contents – Part II XXXIII
A Study of Parallel Approaches in MOACOs for Solving the
BicriteriaTSP . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
316
A.M. Mora, J.J. Merelo, P.A. Castillo, M.G. Arenas,
P.Garćıa-Sánchez, J.L.J. Laredo, and G. Romero
Optimizing Strategy Parameters in a Game Bot . . . . . . . . . .
. . . . . . . . . . . 325A. Fernández-Ares, A.M. Mora, J.J.
Merelo, P. Garćıa-Sánchez, andC.M. Fernandes
Implementation Matters: Programming Best Practices for
EvolutionaryAlgorithms . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
333
J.J. Merelo, G. Romero, M.G. Arenas, P.A. Castillo,A.M. Mora,
and J.L.J. Laredo
Online vs Offline ANOVA Use on Evolutionary Algorithms . . . . .
. . . . . . . 341G. Romero, M.G. Arenas, P.A. Castillo, J.J.
Merelo, and A.M. Mora
Bio-inspired Combinatorial Optimization: Notes on Reactive
andProactive Interaction . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 348
Carlos Cotta and Antonio J. Fernández-Leiva
Applying Evolutionary Computation andNature-inspired Algorithms
to Formal Methods
A Preliminary General Testing Method Based on Genetic Algorithms
. . . 356Luis M. Alonso, Pablo Rabanal, and Ismael Rodŕıguez
Tackling the Static RWA Problem by Using a Multiobjective
ArtificialBee Colony Algorithm . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 364
Álvaro Rubio-Largo, Miguel A. Vega-Rodŕıguez,Juan A.
Gómez-Pulido, and Juan M. Sánchez-Pérez
Applying a Multiobjective Gravitational Search Algorithm
(MO-GSA)to Discover Motifs . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 372
David L. González- Álvarez, Miguel A. Vega-Rodŕıguez,Juan A.
Gómez-Pulido, and Juan M. Sánchez-Pérez
Looking for a Cheaper ROSA . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 380Fernando L. Pelayo,
Fernando Cuartero, and Diego Cazorla
A Parallel Skeleton for Genetic Algorithms . . . . . . . . . . .
. . . . . . . . . . . . . . . 388Alberto de la Encina, Mercedes
Hidalgo-Herrero,Pablo Rabanal, and Fernando Rubio
A Case Study on the Use of Genetic Algorithms to Generate Test
Casesfor Temporal Systems . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 396
Karnig Derderian, Mercedes G. Merayo, Robert M. Hierons,
andManuel Núñez
-
XXXIV Table of Contents – Part II
Experimental Comparison of Different Techniques to
GenerateAdaptive Sequences . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 404
Carlos Molinero, Manuel Núñez, and Robert M. Hierons
Recent Advances on Fuzzy Logic and Soft
ComputingApplications
An Efficient Algorithm for Reasoning about Fuzzy
FunctionalDependencies . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412
P. Cordero, M. Enciso, A. Mora, I. Pérez de Guzmán, andJ.M.
Rodŕıguez-Jiménez
A Sound Semantics for a Similarity-Based Logic
ProgrammingLanguage . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
421
Pascual Julián-Iranzo and Clemente Rubio-Manzano
A Static Preprocess for Improving Fuzzy Thresholded Tabulation .
. . . . . 429P. Julián, J. Medina, P.J. Morcillo, G. Moreno, andM.
Ojeda-Aciego
Non-deterministic Algebraic Structures for Soft Computing . . .
. . . . . . . . . 437I.P. Cabrera, P. Cordero, and M.
Ojeda-Aciego
Fuzzy Computed Answers Collecting Proof Information . . . . . .
. . . . . . . . . 445Pedro J. Morcillo, Ginés Moreno, Jaime
Penabad, andCarlos Vázquez
Implication Triples Versus Adjoint Triples . . . . . . . . . . .
. . . . . . . . . . . . . . . . 453Ma Eugenia Cornejo, Jesús
Medina, and Eloisa Ramı́rez
Confidence-Based Reasoning with Local Temporal Formal Contexts .
. . . 461Gonzalo A. Aranda-Corral, Joaqúın Borrego Dı́az, andJuan
Galán Páez
New Advances in Theory and Applications ofICA-Based
Algorithms
Application of Independent Component Analysis for Evaluation
ofAshlar Masonry Walls . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 469
Addisson Salazar, Gonzalo Safont, and Luis Vergara
Fast Independent Component Analysis Using a New Property . . . .
. . . . . 477Rubén Mart́ın-Clemente, Susana Hornillo-Mellado,
andJosé Luis Camargo-Olivares
Using Particle Swarm Optimization for Minimizing Mutual
Informationin Independent Component Analysis . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 484
Jorge Igual, Jehad Ababneh, Raul Llinares, and Carmen Igual
-
Table of Contents – Part II XXXV
Regularized Active Set Least Squares Algorithm for
NonnegativeMatrix Factorization in Application to Raman Spectra
Separation . . . . . . 492
Rafal Zdunek
A Decision-Aided Strategy for Enhancing Transmissions in
WirelessOSTBC-Based Systems . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 500
Tiago M. Fernández-Caramés, Adriana Dapena,José A.
Garćıa-Naya, and Miguel González-López
Nonlinear Prediction Based on Independent Component
AnalysisMixture Modelling . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 508
Gonzalo Safont, Addisson Salazar, and Luis Vergara
Biological and Bio-inspired Dynamical Systems
Robustness of the “Hopfield Estimator” for Identification of
DynamicalSystems . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
516
Miguel Atencia, Gonzalo Joya, and Francisco Sandoval
Modeling Detection of HIV in Cuba . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 524Héctor de Arazoza, Rachid
Lounes, Andres Sánchez,Jorge Barrios, and Ying-Hen Hsieh
Flexible Entrainment in a Bio-inspired Modular Oscillator for
ModularRobot Locomotion . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 532
Fernando Herrero-Carrón, Francisco B. Rodŕıguez, andPablo
Varona
Dengue Model Described by Differential Inclusions . . . . . . .
. . . . . . . . . . . . 540Jorge Barrios, Alain Piétrus, Aymée
Marrero,Héctor de Arazoza, and Gonzalo Joya
Simulating Building Blocks for Spikes Signals Processing . . . .
. . . . . . . . . . 548A. Jimenez-Fernandez, M.
Domı́nguez-Morales,E. Cerezuela-Escudero, R. Paz-Vicente, A.
Linares-Barranco, andG. Jimenez
Description of a Fault Tolerance System Implemented in a
HardwareArchitecture with Self-adaptive Capabilities . . . . . . .
. . . . . . . . . . . . . . . . . . 557
Javier Soto, Juan Manuel Moreno, and Joan Cabestany
Systems with Slope Restricted Nonlinearities and Neural
NetworksDynamics . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565
Daniela Danciu and Vladimir Răsvan
Bio-inspired Systems. Several Equilibria. Qualitative Behavior .
. . . . . . . . 573Daniela Danciu
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XXXVI Table of Contents – Part II
Interactive and Cognitive Environments
Biologically Inspired Path Execution Using SURF Flow in
RobotNavigation . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
Xavier Perez-Sala, Cecilio Angulo, and Sergio Escalera
Equilibrium-Driven Adaptive Behavior Design . . . . . . . . . .
. . . . . . . . . . . . . 589Paul Olivier and Juan Manuel Moreno
Arostegui
Gait Identification by Using Spectrum Analysis on State
SpaceReconstruction . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 597
Albert Samà, Francisco J. Ruiz, Carlos Pérez, and Andreu
Català
Aibo JukeBox A Robot Dance Interactive Experience . . . . . . .
. . . . . . . . . . 605Cecilio Angulo, Joan Comas, and Diego
Pardo
International Workshop of Intelligent Systems forContext-Based
Information Fusion (ISCIF’11)
On Planning in Multi-agent Environment: Algorithm of
SceneReasoning from Incomplete Information . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 613
Tomasz Grzejszczak and Adam Galuszka
Research Opportunities in Contextualized Fusion Systems. The
HarborSurveillance Case . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 621
Jesus Garcia, José M. Molina, Tarunraj Singh, John Crassidis,
andJames Llinas
Multiagent-Based Middleware for the Agents’ Behavior Simulation
. . . . . 629Elena Garćıa, Sara Rodŕıguez, Juan F. De Paz,
andJuan M. Corchado
A Dynamic Context-Aware Architecture for Ambient Intelligence .
. . . . . 637José M. Fernández, Rubén Fuentes-Fernández, and
Juan Pavón
Group Behavior Recognition in Context-Aware Systems . . . . . .
. . . . . . . . . 645Alberto Pozo, Jesús Graćıa, Miguel A.
Patricio, and José M. Molina
Context-Awareness at the Service of Sensor Fusion Systems:
Invertingthe Usual Scheme . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 653
Enrique Mart́ı, Jesús Garćıa, and Jose Manuel Molina
Improving a Telemonitoring System Based on Heterogeneous
SensorNetworks . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661
Ricardo S. Alonso, Dante I. Tapia, Javier Bajo, and Sara
Rodŕıguez
-
Table of Contents – Part II XXXVII
Supporting System for Detecting Pathologies . . . . . . . . . .
. . . . . . . . . . . . . . 669Carolina Zato, Juan F. De Paz,
Fernando de la Prieta, andBeatriz Mart́ın
An Ontological Approach for Context-Aware Reminders in
AssistedLiving Behavior Simulation . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 677
Shumei Zhang, Paul McCullagh, Chris Nugent, Huiru Zheng,
andNorman Black
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . 685
-
On the Use of Genetic Algorithms to Improve
Wavelet Sign Coding Performance
Ricardo Garćıa1,�, Otoniel López1, Antonio Mart́ı2, and Manuel
P. Malumbres1
1 Miguel Hernández University,Avda. Universidad s/n, 03202,
Elche, Spain
{r.garcia,otoniel,mels}@umh.es2 Universidad Politécnica de
Valencia,
Camino de Vera s/n, 46222, Valencia,
[email protected]
Abstract. Compression of wavelet coefficient sign has been
assumed tobe inefficient for a long time. However, in the last
years several proposalshave been developed and, in fact several
image encoders like JPEG 2000include sign coding capabilities. In
this paper, we present a new signcoding approximation using a
genetic algorithm in order to efficientlypredict the sign of
wavelet coefficients. We have included that predic-tion in a fast
non-embedded image encoder. Preliminary results showthat, by
including sign coding capabilities to a non-embedded encoder,the
compression gain is up to 17.35%, being the Rate-Distortion
(R/D)performance improvement up to 0.25 dB.
Keywords: sign coding, wavelets, image coding, genetic
algorithms.
1 Introduction
Wavelet transforms have proved to be very powerful tools for
image compres-sion. Many state-of-the-art image codecs, including
the JPEG2000 standard [1],employ a wavelet transform in their
algorithms. One advantage is the provisionof both frequency and
spatial localization of image energy. The image energy iscompacted
into a small fraction of the transform coefficients and
compressioncan be achieved by coding these coefficients. The energy
of a wavelet transformcoefficient is restricted to non-negative
real numbers, but the coefficients them-selves are not, and they
are defined by both a magnitude and a sign. Shapirostated in [2]
that a transform coefficient is equally likely to be positive or
nega-tive and thus one bit should be used to encode the sign. In
recent years, severalauthors have begun to use context modeling for
sign coding [3][4][5].
In [5], A. Deever and S. Hemami examines sign coding in detail
in the contextof an embedded wavelet image coder. The paper shows
that a Peak Signal toNoise Ratio (PSNR) improvement up to 0.7 dB is
possible when sign entropy
� Thanks to Spanish Ministry of education and Science under
grant DPI2007-66796-C03-03 for funding.
J. Cabestany, I. Rojas, and G. Joya (Eds.): IWANN 2011, Part I,
LNCS 6691, pp. 505–512, 2011.c© Springer-Verlag Berlin Heidelberg
2011
-
506 R. Garćıa et al.
coding and a new extrapolation technique based on the mutual
information thatbiorthogonal basis vectors provide to improve the
estimation of insignificantcoefficients are combined. However, the
contribution of sign coding by itself tothe PSNR improvement is
only up to 0.4 dB.
In [4] the Embedded Block Coding with Optimized Truncation of
the embed-ded bit-streams (EBCOT), core coding tool of the JPEG
2000 standard, encodesthe sign of wavelet coefficients using
context information from the sign of hori-zontal and vertical
neighbor coefficients (North, South, East, West directions).Five
context are used to model the sign coding stage.
In [3], X. Wu presents a high order context modeling encoder. In
this coder,the sign and the textures share the same context
modeling. This model is basedon a different neighborhood for the
HL, LH and HH wavelet subbands. For theHL subband, the information
of North, North-West, North-East, North-Northand South sign is used
to predict the current coefficient sign. The neighborssign
information used for the LH subband are North, North-West,
North-East,West-West and East. Finally, for the HH subband, an
inter-band prediction isused besides the intra-band prediction used
by the HL and LH subbands.
Genetic algorithms were first introduced by Holland in [6] and
they are nowa-days well known techniques for finding nearly optimal
solutions of very largeproblems and also, they have been used in
image processing [7][8].
In a genetic algorithm, the evolution usually starts from a
population of ran-domly generated individuals and happens in
generations. In each generation,the fitness of every individual in
the population is evaluated by means of a costfunction that
determines the optimal degree we are looking for (i.e
compressionrate). Multiple individuals are stochastically selected
from the current popula-tion (based on their fitness), and modified
(recombined and possibly randomlymutated) to form a new population.
The new population is then used in the nextiteration of the
algorithm. Commonly, the algorithm terminates when either amaximum
number of generations has been produced, or a satisfactory
fitnesslevel has been reached for the population.
In this paper, we will explore the convenience of employing
genetic algorithmsto efficiently predict the wavelet coefficient
signs. If we find a genetic algorithmthat help us to define a good
wavelet sign prediction, then, instead of codingthe sign, we will
encode the result of the prediction (i.e success or failure). Ifthe
sign prediction is really good, a binary entropy encoder will be
able to getsignificant compression rates. So, our goal is to define
a genetic algorithm thatfinds out the paremeters of our sign
predictor that achieve the best predictionperformance. As studied
in the literature, the parameters to be found by ourgenetic
algorithm will be a) the neighbor set that defines the prediction
context,and b) the sign values (sign patterns) of wavelet
coefficient neighbor set withthe correspondent sign prediction for
current wavelet coefficient.
After running the genetic algorithm and configured the sign
predictor, we willevaluate the impact of the sign coding module in
the overall performance of animage wavelet encoder. In particular,
we will use the LTW wavelet encoder [9]to determine the bit-rate
savings for several test images.
-
Genetic Algorithm for Wavelet Sign Coding 507
The remainder of the paper is organized as follows: Section 2
describes our signcoding approximation. In Section 3, we show the
results of the global encodersystem (with sign coding stage) and
compare it with SPIHT and JPEG 2000.Finally, in Section 4 some
conclusions are drawn.
2 Wavelet Sign Prediction
Most wavelet image codecs do not consider the use of sign coding
tools since thewavelet coefficients located at the high frequency
subbands form a zero-meanprocess, and therefore equally likely
positive as negative.
Schwartz, Zandi and Boliek were the first authors to consider
sign coding,using one neighboring pixel in their context modeling
algorithm [10]. The mainidea behind this approach is to find
correlations along and across edges.
The HL subbands of a multi-scale 2-D wavelet decomposition are
formedfrom low-pass vertical filtering and high-pass horizontal
filtering. The high-passfiltering detects vertical edges, thus the
HL subbands contain mainly verticaledge information. Oppositely
defined are the LH subbands that contain primarilyhorizontal edge
information.
As Deever explained in [5], given a vertical edge in an HL
subband, it isreasonable to expect that neighboring coefficients
along the edge have the samesign as the coefficient being coded.
This is because vertical correlation oftenremains very high along
vertical edges in images. When a low-pass filter is appliedalong
the image columns, it results in a series of similar rows, as
elements in arow tend to be very similar to elements directly above
or below due to the highvertical correlation. Subsequent high-pass
filtering along similar rows is expectedto yield vertically
correlated transform coefficients.
It is also important to consider correlation across edges, being
the natureof the correlation directly affected by the structure of
the high pass filter. ForDaubechies’ 9/7 filters, wavelet
coefficient signs are strongly negatively corre-lated across edges
because this filter is very similar to a second derivative of
aGaussian, so, it is expected that wavelet coefficients will change
sign as the edgeis crossed. Although the discrete wavelet transform
involves sub sampling, thesub sampled coefficients remain strongly
negatively correlated across edges. Inthis manner, when a wavelet
coefficient is optimally predicted as a function of itsacross-edge
neighbors (e.g. left and right neighbors in HL subbands), the
opti-mal prediction coefficients are negative, indicating an
expected sign change. Thisconclusion is general for any wavelet
with a shape similar to a second derivativeof a Gaussian.
To estimate sign correlation in a practical way, we have applied
a 6-levelDyadic Wavelet Transform decomposition of the source image
and then a lowquantization level to the resulting wavelet
coefficients. As a first approach andtaking into account that the
sign neighborhood correlation depends on the sub-band type
(HL,LH,HH) as Deever assesses in [5], we have used three
differentneighbors depending on the subband type. So, for HL
subband, the neighborsused are N, NN and W. Taking into account
symmetry, for the LH subband,
-
508 R. Garćıa et al.
those neighbors are W, WW, and N. For the HH subband they are N,
W, andNW, exploiting the correlation along and across the diagonal
edges. This leadus to a maximum of 33 Neighbor Sign Patterns (NSP)
for each subband type.
Table 1. Probability distribution of neighbor sign patterns
(NSPs) of HL6 subband(8x8 coefficients) in Lena image
C N NN W Occurrences %Probability
+ + + + 13 20.31+ + + - 8 12.50- - - + 8 12.50- + + + 6 9.38- -
+ + 6 9.38
Others 23 35.93
In Table 1 we show the NSP probability distribution for HL6
subband (fromthe sixth decomposition level) of Lena test image. As
shown, the probabilitythat the current coefficient (C) is positive
when its N, NN and W neighborsare also positive is around 20%.
Besides, if the N and NN neighbors have thesame sign and the W
neighbor has the opposite sign, the current coefficient (C)has the
opposite sign of its W neighbor with a probability of 25% as shown
inrows two and three in Table 1. The visible sign neighborhood
correlation suggestthat the sign bits of wavelet coefficients are
compressible. Using the previouslymentioned neighborhood for each
subband type, we have developed a geneticalgorithm (GA) in order to
find an accurate sign estimation.
2.1 Genetic Algorithm for Wavelet Sign Prediction
The goal of the desired genetic algorithm would be to find a
table where for eachSign Neigborhood Pattern (Vk) we have a sign
prediction (Si,j) for coefficient Ci,j. There is no an univocal
relationship between a neighbor sign combination, i.enot always for
a same Vk pattern, Si,j is always positive or negative. However,it
is possible that for a Vk pattern, Si,j is more probably to be
positive ornegative. But, the problem is still more complex,
because a sign prediction for aneighbor sign pattern could fit well
for an image and not for others. Therefore,the idea is to find
suboptimal neighbor sign pattern predictions that better fitfor a
representative set of images.
The use of genetic algorithms to compress the sign of wavelet
coefficients istwofold. First, when the number of neighbors used to
analyze the sign correlationgrows or when there is a great number
of images to be used in the analysis, thesearch space is
excessively wide. Second, it is not intuitive to find a way
ofcombining the predictions obtained for several images.
In Fig. 1 we show the genetic algorithm pseudocode for sign
prediction. Firstof all we define each individual, containing a
sign prediction for each 33 NSP, then
-
Genetic Algorithm for Wavelet Sign Coding 509
each NSP sign prediction of each individual of the universe is
randomly initial-ized as a positive or negative sign. Then, during
evolution, sequences mate andmutate to generate new sequences in
the population and best sequences are se-lected for survival on the
basis of their fitness function. The mating of sequencesis
performed through crossover operator, where parents are randomly
selectedand its gens (NSPs) are mixed. The best two individuals,
the ones that exhibitbest prediction performance, are selected for
survival. Individuals can also un-dergo mutation, where a sequence
prediction is randomly modified. Finally, afterperforming the
maximum iterations, the algorithm finishes, obtaining an
opti-mal/suboptimal sign prediction for each NSP. We have performed
the fitnessevaluation over Lena and Barbara test images, because
these images are repre-sentative for both low and high textured
images respectively. Several parametersshould be taken into account
when training a genetic algorithm: The populationsize, the
individuals initialization, the number of iterations performed, the
muta-tion probability, the crossover point, the crossover method,
the selection criteriaof the best sequences to be selected for
survival, etc. We have performed lots oftests varying these
parameters to tune the genetic algorithm. The parametersused to
obtain the sign prediction are: population size (100), individuals
initial-ization (ramdomly), number of iterations (1000), mutation
probability (0.001),crossover point (ramdomly) and crossover method
(best two fitness individualsover four randomly selected
parents).
Individual Structure{sign[NSP];//Prediction array for each
neighbor sign pattern combinationfitness; //indicates the goodness
of the individual}Individual universe[NUM-POPULATION]; //Individual
array
function SignPrediction (SubbandType, ImageFiles, mutation
Probability)//Initialization phase: sign[NSPs]=
random(POSITIVE/NEGATIVE)Initialize(universe, NUM-POPULATION,
NSP);//we evaluate each individual of the universe. For each image
in ImageFilesEvaluateFitness(SubbandType, ImageFiles, universe);for
i=0 to NUM-ITERATIONS//Select the best two individuals from
universe for survival.
best =
SelectBestIndividuals(2);//CrossovercrossPoint=random(NSP);//randomly
selects a father and a mother to mix its
gensSelectFatherAndMother(random(NUM-POLUTATION));universe =
MergeFatherAndMother(crossPoint);Mutation(universe, mutation
Probability);universe = universe +
best;EvaluateFitness(SubbandType, ImageFiles, universe);
end//Finally get the best individual.best =
SelectBestIndividuals(1);
end of function
Fig. 1. Genetic algorithm for sign prediction
-
510 R. Garćıa et al.
After running the genetic algorithm for each subband type, we
obtain anindividual containing the prediction of the current
coefficient sign (ŜCi,j [k]),for each NSP (k) of each subband
type. So, what we are going to encode isthe correctness of this
prediction, i.e., a binary valued symbol from ŜCi,j [k] ·SCi,j
(see Table 2). In order to compress this binary valued symbol, we
use twocontexts in the arithmetic encoder for each subband type,
distributing all signcoding predictions from NSPs between them so
as to minimize the zero orderentropy of both contexts. The
selection criterion is to isolate in one context thoseNSPs with the
highest correctness prediction probability and highest numberof
occurrences derived from the probability distribution found in the
previousanalysis. The rest of them are grouped into the other
context. However, thereare certain NSPs with low correctness
probability but with a great amount ofoccurrences, so we have to
heuristically determine the convenience of includingthem in the
first context or not.
Table 2. Sign prediction for HL subband in Lena image for some
NSPs
NSP(k) N NN W Prediction
(ŜCi,j [k])
0 * * * -. . .
13 + + + +14 + + - +
. . .26 - - - +
3 Performance Evaluation
In this section we analyze the behavior of the sign coding when
implemented onLTW image encoder [9]. This new encoder
implementation is called S-LTW. Wewill also compare the S-LTW
encoder versus JPEG2000 (Jasper 1.701.0) andSPIHT (Spiht 8.01) in
terms of R/D and coding delay. All encoders have beentested on an
Intel PentiumM Dual Core 3.0 GHz with 2 Gbyte RAM memory.
In Table 3 we show the relative compression gain with respect to
the originalLTW due only to the sign coding capability for several
test images. As we cansee, the maximum sign compression gain is
17.35%. Furthermore, we show anestimation of the bit savings for
SPIHT encoder.
In Figure 2 we show the R/D improvement when comparing original
LTW ver-sus JPEG2000/SPIHT and S-LTW versus JPEG2000/SPIHT. As
shown, thereis an increase in the PSNR difference between SPIHT and
the new S-LTW en-coder, and regarding JPEG2000, we can see than now
S-LTW has a minor lossin PSNR than original LTW. Regarding coding
delay, the use of a higher contextmodeling in the arithmetic
encoder implies a higher computational cost. In orderto compensate
the coding speed loss, we have changed the arithmetic encoderstage
by a fast arithmetic encoder [11]. As it can be seen in Table 4,
S-LTW
-
Genetic Algorithm for Wavelet Sign Coding 511
0.4
0.5 S-LTW vs SPIHTLTW vs SPIHT
0.3S-LTW vs JPEG2000LTW vs JPEG2000
0.1
0.2
dB)
0 1
00 0.5 1 1.5 2PS
NR
(d
-0.2
-0.1P
-0.4
-0.3
-0.5
0.4
Bit-rate (bpp)
Fig. 2. PSNR-Gain for Bike image
Table 3. Sign compression performance at different bit-rates
Bit-rate S-LTW SPIHT %Gain(bpp) #Significant #Bits #Significant
#Bits
Coefficients Saved Coefficients Saved
Barbara (512x512)
1 45740 7936 54657 9482 17.350.5 22331 3648 27535 4499 16.340.25
10484 1520 13460 1951 14.500.125 4343 304 6016 421 7.00
Bike (2048x2560)
1 855266 115200 1371280 184711 13.470.5 412212 64424 798202
124758 15.630.25 198943 30472 366927 56213 15.320.125 91767 11992
162990 21302 13.07
Table 4. Coding delay (seconds)
Bit-rate JPEG SPIHT LTW S-LTW(bpp) 2000 Orig.
CODING Barbara (512x512)1 0.080 0.042 0.037 0.023
0.5 0.076 0.026 0.022 0.0140.25 0.074 0.018 0.013 0.0090.125
0.073 0.014 0.010 0.006
CODING Bike (2048x2560)1 2.623 0.920 0.647 0.430
0.5 2.543 0.521 0.381 0.2590.25 2.507 0.323 0.224 0.1620.125
2.518 0.221 0.158 0.117
-
512 R. Garćıa et al.
encoder is 49% faster on average in the coding process than
SPIHT encoder and86% faster on average than JPEG2000. Furthermore,
S-LTW encoder is evenfaster than the original LTW version which
does not include the sign codingstage (1.5 times faster on average
in the coding process).
4 Conclusions
We have presented a genetic algorithm that is able to find a
good sign predictorof wavelet coefficient sign. So, by encoding the
sign prediction result (successor failure) with an arithmetic
encoder, the sign information will be highly com-pacted in the
final bitstream. To prove our proposal we have implemented itover
the LTW encoder. The new S-LTW proposed encoder has slightly
betterR/D performance(up to 0.25 dB), or in terms of bitstream, it
is able to reduceit up to 17% for the same quality level. Regarding
coding delay, the new imageencoder is on average 2 times as fast as
SPIHT in the coding process and 1.5times as fast as original
LTW.
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front-matter66910505On the Use of Genetic Algorithms to Improve
Wavelet Sign Coding PerformanceIntroductionWavelet Sign
PredictionGenetic Algorithm for Wavelet Sign Prediction
Performance EvaluationConclusionsReferences