1 biased support vector machine for relevance feedback in image retrieval hoi, chu-hong steven hoi,...

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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


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]


MotivationMotivation

A case of regular SVM Imbalance dataset problem? • # negative >> # positive• Positive overwhelmed by negative

margin

Optimal separating hyperplane


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


Biased SVMBiased SVM• Problem formulation

– Training data:

– The objective function

c

R


Biased SVM (cont.)Biased SVM (cont.)• Optimization by Lagrange multipliers

• Take the partial derivatives of L with respect to R,ξ,c, andρ:


Biased SVM (cont.)Biased SVM (cont.)• Dual problem (Quadratic Programming (QP) )

• The decision functionf (x)>=0

f (x)<0


Relevance Feedback by Biased SVMRelevance Feedback by Biased SVM• One of differences with regular SVM

• Visual comparison

Biased SVM:

Regular SVM:


Relevance Feedback by Biased SVMRelevance Feedback by Biased SVM• Obtained decision function

• Simplified evaluation function


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


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


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.


Experimental Results (cont.)Experimental Results (cont.)

Synthetic dataset 20-Cat COREL Images



50-Cat COREL Images


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.


Thank you!

Budapest, Hungary, July, 2004


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.

1 biased support vector machine for relevance feedback in image retrieval hoi, chu-hong steven hoi,...

Documents

relevance feedback rf

retrieval results

rf regular binary svm

zhang01 slide

bias control slide

objective function c

negative information

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