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ECE160 Spring 2009 Lecture 20 1 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval
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ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

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Page 1: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160 Spring 2009 Lecture 20 1

ECE160 / CMPS182

Multimedia

Lecture 20: Spring 2009Image Recognition and

Retrieval

Page 2: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 2

National Research Priorities Energy Technologies

Fuel efficient engines Replacement energy to fossil fuels Lighter, longer-duration batteries

Bioengineering/Bioinformatics Genes disease Disease medicine

Search with Multimedia Content Video surveillance Photo interpretation

Page 3: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 3

Multimedia Recognition Video surveillance Photo interpretation

Page 4: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 4

Wide-area Surveillance

advertisement of objectvideo.com

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Surveillance Scenarios(1) Intrusion Detection(1) Intrusion Detection (2) Passenger Screening(2) Passenger Screening

(3) Perimeter Monitoring(3) Perimeter Monitoring

ZZ

Use biometric facial recognition to identify individuals of interest through existing closed circuit TV surveillance

Monitor and alert on tailgating, loitering, exit/closed entry, other unauthorized access

Object tracking and biometric facial recognition to determine vehicles and humans exhibiting suspicious behavior

(4) Unattended Baggage(4) Unattended Baggage

Copyright © 2004 Proximex Corp.Copyright © 2004 Proximex Corp.

Identify unattended baggage (or other objects) left for long periods of time

Page 6: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 6

Multimedia Recognition Video surveillance Photo interpretation

Page 7: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 7

How to Organize these Photos?

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Keyword-based Manual labeling is subjective,

cumbersome The aliasing problem

Content-based Promising for general semantics:

outdoor, landscape, flowers, people, etc. Not enough for wh-queries (where, who,

when, or what)

Image Organization & Retrieval

Page 9: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

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EXTENTTM = contEXT + contENT

Context Spatial (location) Temporal Social Others

Content Perceptual features, such as color,

texture, and shape Holistic features and local features

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ECE160Spring 2009 Image Recognition 10

EXTENTTM

Content

Spatial Temporal

Social Others

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Augmented Images

+

Cameraphones with high-quality lens can record location, time, camera parameters, and voice

=

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Context from Space/Time

GPS or CellID data Into place names

Time-based grouping Into meaningful “events”

From place names and time Time of day Weather

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Example of Using Three Pieces of

Information

Content

Spatial Temporal

Page 14: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 14

Maui Sunsetscan be obtained from Space/Time

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Use content for verification

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Use content to transfer metadata

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Summarize of the example

Derived from Context Derive time of the day Obtain weather Verify content

Use of Content Verify context Transfer context

Much more…

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Are They Similar?

Page 19: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 19

Are They Similar?

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ECE160Spring 2009 Image Recognition 20

Are They Similar?

In terms of what? What is the user’s perception?

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Conveying Perception

Image Databases Conveyed via Examples

Use a sunset picture (or pictures) to find more sunset images

Where does the perfect example come from?

Page 22: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 22

Conveying Perception

Internet Searches Conveyed via Keywords

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Keyword Retrieval

Pros A user-friendly paradigm

Cons Annotation is a laborious process Annotation quality can be subpar Annotation can be subjective Synonyms

Page 24: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 24

Conveying Perception

Image Databases Conveyed via Examples

Use a sunset picture (or pictures) to find more sunset images

Where does the perfect example come from?

Page 25: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 25

Are They Similar?

Page 26: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 26

Are They Similar?

Page 27: ECE160 Spring 2009Lecture 201 ECE160 / CMPS182 Multimedia Lecture 20: Spring 2009 Image Recognition and Retrieval.

ECE160Spring 2009 Image Recognition 27

Are They Similar?

In terms of what? What is the user’s perception?

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Recogintion of Content

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Recognition

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Recognition

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clouds vs. waves

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Web 1.0 vs. Web 2.0

User

User User

Content

Content (Image/Video)

Content

Content

Content

Content

Content

Content (text)

Content

Content

Content

Content

Content

User

UserUser User

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Web 2.0 Content + Users + Interactions Collect rich, organized content Attract users & interactions

To provide metadata To provide new content

Improve search quality With new metadata and data Via social-network structure

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User management

User management

Photo uploading

Single/multiple

Upload wizard

Photo uploading

Building social network

Social networks

Event management

Event management

Photo search

Photo search

Metadata collectionMetadata fusionImage annotation

Metadata collection - contextual - content

Metadata fusion

Annotate photos

External functionalities

Internal functionalities

Fotofiti