Generic Framework for Context-Dependent Fusion with Application to Landmine Detection Ahmed Chamseddine Ben Abdallah Multimedia Research Lab CECS Department University of Louisville June 2009 1
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Generic Framework for Context-Dependent Fusion with Application to Landmine Detection Ahmed Chamseddine Ben Abdallah Multimedia Research Lab CECS Department.
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Slide 1
Generic Framework for Context-Dependent Fusion with Application
to Landmine Detection Ahmed Chamseddine Ben Abdallah Multimedia
Research Lab CECS Department University of Louisville June 2009
1
Slide 2
Outline Motivational Example Related Work Global Fusion Local
Fusion Contributions Context-Extraction for Local Fusion (CELF)
CELF with Feature Discrimination (CELF-FD) Application to Landmine
detection Conclusions and Future Work 2
Slide 3
MOTIVATIONAL EXAMPLE 3
Slide 4
Global Fusion: Ask the audience or the wisdom of crowds
Majority Voting 4
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Example 1 5
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Example 2 6
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Example 3 (1) 7
Slide 8
Expert selection: Call a friend Doctor News Music Movies
EconomistMusician Literature Artist CECS Phd Student Nurse Fashion
Music Fashion Politics Basketball player Cooking 8
Slide 9
Medical question What is the scientific name of the swine Flu
virus? A: H1N1B: H3N2 C: H2N2D: H5N1 A: H1N1 A A A A C B B B C C D
C 9 Doctor News Music Nurse
Slide 10
Medical question What is best-selling music album worldwide? A:
Come on OverB: Thriller C: Falling into YouD: Daydream B: Thriller
B A A A C B B B C C D C 10 Doctor News Music Musician Fashion
Music
Slide 11
Medical question Which of the following is not a prime number?
A: 19B: 29 C: 39D: 59C: 39 A A A A A B B B C C D C 11 Economist
CECS Phd Student
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Hey I know about fashion! Hey I know about Music! 12
Slide 13
Conclusions Multiple sources better than single source need for
fusion. Local fusion is better than global fusion. Grouping
experts. Identifying the context/domain. Combining the experts
decision. 13
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RELATED WORK Global Fusion 14
Slide 15
Related work Global fusion When combining multiple independent
and diverse decisions each of which is at least more accurate than
random guessing, random errors cancel each other out, correct
decisions are reinforced. 15
Slide 16
Related work Classifier fusion architecture 16 Combiner
Decision Level Feature Level Data Level Raw Data 1 Feature
Extraction 1 Classifier 1 Raw Data 2 Feature Extraction 2
Classifier 2 Raw Data K Feature Extraction K Classifier K
Slide 17
Related work Global Fusion approaches Bayesian Fusion ANN
Fusion Borda Count Fusion Dempster-Shafer Fusion Decision Template
Fusion Fuzzy Integral 17
Slide 18
RELATED WORK Local Fusion 18
Slide 19
Related work Local fusion Divide and Conquer approach 19
Context extraction Context extraction Decision fusion Decision
fusion Feature set Global decision Classifier output Two
independent tasks
Slide 20
Related work Local Fusion approaches Category 1: find the
neighborhood of the testing sample and create a fusion model in the
testing phase Dynamic classifier by local accuracy, time consuming
Category 2: cluster and create fusion models in the training phase
Clustering and selection, Context-Dependent Fusion, . Treats the
context extraction and the decision fusion components
independently. 20
Slide 21
CONTRIBUTIONS 21
Slide 22
Contributions 1. A local fusion approach (CELF) based on a
novel objective function that combines Context identification
Multi-algorithm fusion 2. CELF with adaptive feature weight
assignment (CELF-FD) 3. Application to landmine detection 22
Slide 23
CONTRIBUTIONS 1- Context Extraction for Local Fusion (CELF)
23
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ContributionsNotations 24 Feature Extraction 1 Classifier 1
Feature Extraction 2 Classifier 2 Feature Extraction K Classifier K
Data sample j Ground truth t j (xj)1(xj)1 (xj)2(xj)2 (xj)K(xj)K y
j1 y j2 y jK (x j, y j, t j ) Feature vector Decision vector Ground
truth
Slide 25
ContributionsClustering FCM Algorithm Update c i Update u ij
where 25 x c1c1 x c2c2 x c3c3 x1x1 x2x2 x3x3 x4x4 x5x5 x6x6 x7x7
x8x8 x9x9 x 10 x 11 x 13 x 12 x 14 x 15 (u 1,5,u 2,5,u 3,5 )
Contributions Update Equations (2) 31 Optimizing J w.r.t the
membership yields Deviation from desired output Clusters with
consistent fusion weights Similarity in the feature space Compact
clusters
Contributions CELF Algorithm Initialize U and W. repeat Update
cluster centers. Update W. Update U. until stopping condition
satisfied return Centers, U, W 33
Slide 34
Contributions Illustrative example (1) 34 Classifier 1
Classifier 2 Decision space Feature space
Slide 35
Contributions Illustrative Example (2) 35
Slide 36
Contributions Illustrative Example (3) 36 Classifier 1
Classifier 2 CELF Y P(Y)
Slide 37
CONTRIBUTIONS 2- Context Extraction for Local Fusion with
feature discrimination (CELF) 37
Slide 38
Contributions Context Extraction for Local Fusion with feature
discrimination (CELF-FD) For high dimensional spaces, standard
clustering algorithms cannot generate a meaningful partition. To
alleviate this drawback, we introduce feature weighting aspect.
CELF-FD combines: Clustering Feature Discrimination Selection of
local expert classifiers 38
Slide 39
ContributionsNotations V set of features weights. 39 Cluster i
Features from classifier l
Slide 40
Contributions Objective Function 40 Feature discrimination
Optimized in CELF-FD
Slide 41
Contributions CELF-FD Algorithm Initialize U, V and W. repeat
Update cluster centers. Update W. Update U. Update V. until
stopping condition satisfied return Centers, U, V, W 41
Slide 42
Contributions Toy data (1) 42
Slide 43
Contributions Toy data (2) Classifier 1 Accuracy 69% Classifier
2 Accuracy 81% 43
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Contributions Experimental results using CELF (1) 44
Slide 45
Contributions Experimental results using CELF (2) 45 Y
P(Y)
Slide 46
CONTRIBUTIONS 3- Application to landmine detection 46
Slide 47
Contributions Landmine problem Objective: analyze data
collected by multiple sensors and make a decision if there is
buried mine. Different mine types Soil properties: Asphalt, gravel,
sand Varying density Water held by vegetation roots Rain, snow
Various minerals 47
Slide 48
Contributions Ground Penetration component 48 Autonomous Mine
Detection System Vehicle
Slide 49
Contributions WEMI Data taken at 21 frequencies
(logarithmically spaced from 330 Hz to 90.03 KHz). 49 Magnitude
Frequency Blank NMC LM mineHMC
Slide 50
Contributions Landmine detectors Landmine detectors using GPR
EHD SCF HMM Landmine detector using WEMI 50
Slide 51
Contributions GPR Algorithm 1: Edge Histogram Descriptor 51
Based on the EHD used in the MPEG-7 standard. Encodes spatial
distribution of edges in a 3-D volume (down-track &
cross-track) Edges are categorized into 4 types: {V, H, 45 o, -45 o
}. Uses a possibilistic K-NN rule for confidence assignment.
Slide 52
Contributions GPR Algorithm 2: HMM-based Treats the down-track
dimension as the time variable. A sequence of 15 observation
vectors is produced for each alarm. Each observation has 4
features: (+/- Diag. edges & +/- Anti-diag edges) 52. MINE
STATE 1 MINE STATE 2 MINE STATE 3 STATES X1 X2 X3... X14 X15 MINE
STATE 1 MINE STATE 2 MINE STATE 3
Slide 53
Contributions GPR Algorithm 3: Spectral Detector Captures the
characteristics of a target in the frequency domain. Extracts the
alarm Spectral Correlation Feature (SCF). Assigns a confidence
value based on similarity to prototypes that characterize mine
objects. 53
Slide 54
Contributions WEMI Detection Algorithm Extracts 4 features: One
model fitting parameter ( ) Fitting error 2 spread features (Q
spread, T spread ) A Neural Network classifier was trained using
the above 4 features to assign a confidence value to each alarm.
54
Slide 55
Contributions CELF Architecture 55
Slide 56
Contributions Data collection Data Collected from 2 different
sites 864 Alarms: 308 mines, classified into 4 categories :
Anti-tank with high metal content (ATM) Anti-tank with low metal
content (ATLM) Anti-personal with high metal content (APM)
Anti-personal with low metal content (APLM) 556 False Alarms,
classified into 3 categories: High metal clutter (HMC) Non-metal
clutter (NMC) Blank Targets buried up to 5 inches deep. 56
Slide 57
Contributions Motivation for fusion 57
Slide 58
Contributions Fusion results (1) 58
Slide 59
Contributions Fusion results (2) 59
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Contributions Fusion results (3) 60
Slide 61
Contributions CELF for a Vehicle Mounted GPR System 61
Slide 62
Contributions Fusion results 62
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CONCLUSIONS & FUTURE WORK 63
Slide 64
Conclusions A new local fusion approach based on a novel
objective function that combines: Context identification
(clustering component). Multi-algorithm fusion (classification
component). Two variants: CELF and CELF-FD. Promising results on
synthetic data on landmine detection problem. 64
Slide 65
Future Work Investigate other clustering techniques Kernel
clustering, mahalanobis distance, Dirchlet distribution Integrate
other fusion techniques Fuzzy integral Optimize the number of
clusters CA, AIC Generalize the algorithm to support data with more
classes. Optimize . 65
Slide 66
Questions? A. C. Ben Abdallah, H. Frigui and P. Gader "Context
Extraction for Local Fusion using Fuzzy Clustering and Feature
Discrimination", Fuzz-ieee, Korea, April 2009. H. Frigui, A. C. Ben
Abdallah, and P. Gader "Context-dependent fusion for landmine
detection with multisensor systems", SPIE, Detection and Sensing of
Mines, Explosive Objects, and Obscured Targets XIV, Orlando, April
2009. H. Frigui, J. Caudill, and A. C. Ben Abdallah, "Fusion of
Multi-Modal Features for Efficient Content-Based Image Retrieval",
IEEE World Congress on Computational Intelligence (WCCI 2008), Hong
Kong, June 2008. H. Frigui, P. Gader, and A. C. Ben Abdallah, "A
Generic Framework for Context-Dependent Fusion with Application to
Landmine Detection", SPIE Defense and Security Symposium, Orlando,
March 2008. 66
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Objective Function 67 Feature discrimination Cluster-dependent
Cluster-dependent
Slide 68
Experimental results using other local fusion method 68