Machine Learning. for Image Retrieval. Edward Chang Associate Professor, Electrical Engineering, UC Santa Barbara CTO, VIMA Technologies
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1 Machine Learning for Image Retrieval Edward Chang Associate Professor, Electrical Engineering, UC Santa Barbara CTO, VIMA Technologies 4/5/2004 JHU-APL 1
2 Are They Similar? 4/5/2004 JHU-APL 2
3 Are They Similar? In terms of what? Is Tiger Woods more similar to Michael Jordan than to Bill Gates? 4/5/2004 JHU-APL 3
4 Conveying In Terms of What Relational Databases Conveyed via Query Languages Example Select * where colors = (blue v pink v white) & (textures = coarse v horizontal) & (shapes = people + tennis rackets) 4/5/2004 JHU-APL 4
5 Conveying In Terms of What Image Databases Conveyed via Examples Use a sunset picture (or pictures) to find more sunset images Where does the perfect example come from? 4/5/2004 JHU-APL 5
6 Conveying In Terms of What Internet Searches Conveyed via Keywords 4/5/2004 JHU-APL 6
7 4/5/2004 JHU-APL 7
8 4/5/2004 JHU-APL 8
9 4/5/2004 JHU-APL 9
10 Query by Keywords Pros A user-friendly paradigm Cons Annotation is a laborious process Annotation quality can be subpar Annotation can be subjective Synonyms 4/5/2004 JHU-APL 10
11 Query Specification Paradigms Query by SQL-like languages Query by examples Query by keywords Query by nothing! 4/5/2004 JHU-APL 11
12 Image Retrieval Demo ACM SIGMOD 01 ACM MM 01, 02 IEEE CVPR 03 NSF Paris, Harvard, DC, Seattle Workshops 4/5/2004 JHU-APL 12
13 Outline Query Paradigms Q-by-Nothing Demo Technical Challenges Preliminary Results Technology Summary 4/5/2004 JHU-APL 13
14 Technical Challenges Learn a complex and subjective query concept Formulate a distance function to measure perceptual similarity 4/5/2004 JHU-APL 14
15 Classical Statistical Models [Donoho 2000] N:Number of training instances D:Dimensionality Classical models assume N >> D N N - N + 4/5/2004 JHU-APL 15
16 Image (Information) Retrieval N < D N + << N - 4/5/2004 JHU-APL 16
17 Solutions N < D Make each u in U most informative ACM TIOS 2003, ACM MM 2001 Increase N - through co-training PCM 2002, ICIP 2003 Reduce D ACM MM 2002 (DPF) N + << N - Conformal transformation Kernel boundary alignment ACM MM 2003, ICML 2003 Advanced Methods 4/5/2004 JHU-APL 17
18 Query Concept 4/5/2004 JHU-APL 18
19 Traditional Random Sampling 4/5/2004 JHU-APL 19
20 MEGA s S- & F-Step 4/5/2004 JHU-APL 20
21 SVMActive 4/5/2004 JHU-APL 21
22 SVMActive 4/5/2004 JHU-APL 22
23 SVMActive 4/5/2004 JHU-APL 23
24 SVMActive 4/5/2004 JHU-APL 24
25 Ranking 4/5/2004 JHU-APL 25
26 Solutions N < D Make each u in U most informative ACM TIOS 2003, ACM MM 2001 Increase N - through co-training PCM 2002, ICIP 2003 Reduce D ACM MM 2002 (DPF) N + << N - Conformal transformation Kernel boundary alignment ACM MM 2003, ICML 2003 Advanced Methods 4/5/2004 JHU-APL 26
27 A Checkerboard Experiment + :: Majority o :: Minority Imbalanced Ratio Majorities = Minorities 10:1 4/5/2004 JHU-APL 27
28 A Checkerboard Experiment + :: Majority o :: Minority Imbalanced Ratio Majorities = Minorities ideal 10:1 4/5/2004 JHU-APL 28
29 A Checkerboard Experiment + :: Majority o :: Minority Imbalanced Ratio Majorities = Minorities 10:1 4/5/2004 JHU-APL 29
30 Boundary Bias in SVMs Majority Majority Minority Minority 4/5/2004 JHU-APL 30
31 Bayesian Explanation Bayes Decision Rule ( ω x) p ( x ω ) p( ω ) + + p + = p p ( x ω ) ( x ω ) + p p p( x) ( ω ) ( ω ) + p p ( ω x) = p ( x ω ) p( ω ) p( x) ( ω ): likelihood of ( ω ): prior of classω p x classω When N- >> N+, p(ω ) >> p(ω + ) 4/5/2004 JHU-APL 31
32 4/5/2004 JHU-APL 32 Three Strategies [ICML 2003] + = = b K f n i i i i 1 ), ( y ) ( sgn x x x α b : intercept b : influence of x i i K : kernel function α
33 Imbalanced Data Set SV- Majority Minority ideal trained SV+ 4/5/2004 JHU-APL 33
34 After Alignment Majority KBA trained Minority ideal 4/5/2004 JHU-APL 34
35 Technical Challenges Learn a complex and subjective query concept Formulate a distance function to measure similarity 4/5/2004 JHU-APL 35
36 Are They Similar? 4/5/2004 JHU-APL 36
37 Perceptual Distance Function Two Monumental Challenges Formulating a perceptual feature space Formulating a perceptual distance function 4/5/2004 JHU-APL 37
38 Distribution of Distances 4/5/2004 JHU-APL 38
39 Minkowski Distance Objects P and Q D = (Σ M (pi -qi) n ) 1/n Similar images are similar in all M features 4/5/2004 JHU-APL 39
40 4/5/2004 JHU-APL E E E E E E Feature Distance Frequency 1.0E E E E E E Feature Distance Frequency
41 Weighted Minkowski Distance D = (Σ M wi(pi -qi) n ) 1/n Similar images are similar in the same subset of the M features 4/5/2004 JHU-APL 41
42 Average Distance GIF Feature Number Scale 0 up/down Feature Number Average Distance Average Distance Cropping Rotation Feature Number Feature Number Average Distance /5/2004 JHU-APL 42
43 Similarity Theories Objects are similar in all respects (Richardson 1928) Objects are similar in some respects (Tversky 1977) Similarity is a process of determining respects, rather than using predefined respects (Goldstone 94) 4/5/2004 JHU-APL 43
44 DPF Which Place is Similar to New York? Partial Dynamic Dynamic Partial Function 4/5/2004 JHU-APL 44
45 Precision/Recall 4/5/2004 JHU-APL 45
46 Search: Image Piracy Detection Corbis filed a multi-million dollar lawsuit against Amazon.com, accusing the e-commerce giant of the selling its images without its consent. InternetNews.com, July 1, 2003 Unlicensed The Corbis suit is seeking up to $150,000 for each copyrighted work infringed 4/5/2004 JHU-APL 46
47 4/5/2004 JHU-APL 47
48 4/5/2004 JHU-APL 48
49 VIMA Visual Search 4/5/2004 JHU-APL 49
50 Current Other Work Video Surveillance and Sensor Networks Context-based Distance Function Learning High-dimensional Indexing Scalability Speeding up SVMs 4/5/2004 JHU-APL 50
51 IR Key Components Learners Multimodal Integration Perceptual Distance Function High D Indexers 4/5/2004 JHU-APL 51
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