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Biased Support Vector Machine forBiased Support Vector Machine for
Relevance Feedback in Image Retrieval Relevance Feedback in Image Retrieval
Hoi, Chu-Hong StevenHoi, Chu-Hong Steven
Department of Computer Science & EngineeringDepartment of Computer Science & EngineeringThe Chinese University of Hong KongThe Chinese University of Hong Kong
Shatin, Hong KongShatin, Hong Kong
Budapest, 25-29 July, 2004Budapest, 25-29 July, 2004
Presentation in IJCNN 2004
2IJCNN04IJCNN04 :: Biased Support Vector Machine for Relevance Feedback in Image RetrievalBiased Support Vector Machine for Relevance Feedback in Image Retrieval
OutlineOutline• Background & Motivation• Biased Support Vector Machine (Biased SVM)• Relevance Feedback by Biased SVM• Experimental Results• Conclusions
3IJCNN04IJCNN04 :: Biased Support Vector Machine for Relevance Feedback in Image RetrievalBiased Support Vector Machine for Relevance Feedback in Image Retrieval
BackgroundBackground• Challenges in Content-based Image Retrieval (CBIR)
– Semantic gap, low-level features, high-level concepts– Subjectivity of human being, …
• Relevance Feedback (RF)– Refine retrieval results by incorporating users’ interactions– A technique to narrow down the semantic gap, subjectivity– Methods: heuristic weighting [Rui98, MARS99], optimization
[MindReader98, Rui00], classification [MacArthur99], other learning techniques [Huang01], …
– Popular method proposed recently: Support Vector Machines (SVM) [Hong00, Chen01, Tong01, Zhang01]
4IJCNN04IJCNN04 :: Biased Support Vector Machine for Relevance Feedback in Image RetrievalBiased Support Vector Machine for Relevance Feedback in Image Retrieval
MotivationMotivation
A case of regular SVM Imbalance dataset problem? • # negative >> # positive• Positive overwhelmed by negative
margin
Optimal separating hyperplane
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MotivationMotivation• Limitation of regular SVMs for RF
– Regular binary SVM• Simply treat as a strict binary classification problem • without imbalance consideration
– Regular 1-SVM • Exclude negative information
• Our solution: Biased SVM– A modified 1-SVM incorporating negative information with bias
control
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Biased SVMBiased SVM• Problem formulation
– Training data:
– The objective function
c
R
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Biased SVM (cont.)Biased SVM (cont.)• Optimization by Lagrange multipliers
• Take the partial derivatives of L with respect to R,ξ,c, andρ:
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Biased SVM (cont.)Biased SVM (cont.)• Dual problem (Quadratic Programming (QP) )
• The decision functionf (x)>=0
f (x)<0
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Relevance Feedback by Biased SVMRelevance Feedback by Biased SVM• One of differences with regular SVM
• Visual comparison
Biased SVM:
Regular SVM:
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Relevance Feedback by Biased SVMRelevance Feedback by Biased SVM• Obtained decision function
• Simplified evaluation function
11IJCNN04IJCNN04 :: Biased Support Vector Machine for Relevance Feedback in Image RetrievalBiased Support Vector Machine for Relevance Feedback in Image Retrieval
Experimental ResultsExperimental Results• Datasets
– One synthetic dataset: 40-Cat, each contains 100 data points randomly generated by 7 Gaussian in a 40-dimensional space.
– Two real-world image datasets selected from COREL image CDs
• 20-Cat: 2,000 images• 50-Cat: 5,000 images
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Experimental Results (cont.)Experimental Results (cont.)• Image Representation
– Color Moment• 9-dimension
– Edge Direction Histogram• 18-dimension• Canny detector• 18 bins, each of 20 degrees
– Wavelet-based texture • 9-dimension• Daubechies-4 wavelet, 3-level DWT• 9 subimages to generate the feature
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Experimental Results (cont.)Experimental Results (cont.)• Compared Schemes
– Relevance Feedback by regular nu-SVM– Relevance Feedback with 1-SVM– Relevance Feedback with Biased SVM
• Experimental Setup– Metric: Average precision = #relevant / #returned– Pick 10 instances, label pos. or neg.– First iteration, 2 pos. and 8 neg.– Same kernel and settings for compared schemes– 200 relevance feedback simulation rounds are executed
for each compare scheme.
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Experimental Results (cont.)Experimental Results (cont.)
Synthetic dataset 20-Cat COREL Images
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Experimental Results (cont.)Experimental Results (cont.)
50-Cat COREL Images
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Experimental Results (cont.)Experimental Results (cont.)
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ConclusionsConclusions• Address the imbalance problem of relevance feedback
in CBIR.
• Propose a modified SVM technique, i.e. Biased SVM, to attack the imbalance problem of relevance feedback problem in CBIR.
• Demonstrate effectiveness of the proposed scheme from experiments.
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Thank you!
Budapest, Hungary, July, 2004
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ReferencesReferences• [Rui98] Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra, “Relevance Feedback: A Power Tool in Interactive Content-
Based Image Retrieval”, IEEE Tran Circuits and Systems for Video Technology, Vol 8 No 5, 1998, 644-655
• [MARS99] K. Porkaew, S. Mehrotra, and M. Ortega, “Query Reformulation for Content Based Multimedia Retrieval in MARS”, IEEE Int’l Conf. Multimedia Computing and Systems (ICMCS’99), June, 1999
• [MindReader98] Y. Ishikawa, R. Subramanya, and C. Faloutsos, “MindReader: Query databases through multiple examples”, 24th VLDB Conf. (New York), 1998
• [Zhang01] L. Zhang, F. Lin, and B. Zhang, “SUPPORT VECTOR MACHINE LEARNING FOR IMAGE RETRIEVAL”, ICIP’2001, 2001
• [Rui00] Y. Rui, T. S. Huang, “Optimizing learning in image retrieval”, CVPR’00, Hilton Head Island, SC, June 2000
• [MacArthur99] S. MacArthur, C. Brodley, and C. Shyu, “Relevance Feedback Decision Trees in Content-Based Image Retrieval,” workshop CBAIVL, CVPR’00, June 12, 2000.
• [Tong01] S. Tong, and E. Chang, “Support vector machine active learning for image retrieval”, ACM MM’2001, 2001
• [Chen01] Y. Chen, X. S. Zhou, T. S. Huang, “One-class SVM for Learning in Image Retrieval”, ICIP'2001, Thessaloniki, Greece, October 7-10, 2001
• [Hong00] P. Hong, Q. Tian, T. S. Huang, "Incorporate Support Vector Machines to Content-Based Image Retrieval with Relevance Feedback", ICIP'2000, Vancouver, Sep 10-13, 2000.