Orthogonal complement component analysis for positive samples in SVM based relevance feedback image retrieval

Relevance feedback (RF) is an important tool to improve the performance of content-based image retrieval system. Support vector machine (SVM) based RF is popular because it can generalize better than most other classifiers. However, directly using SVM in RF may not be appropriate, since SVM treats the positive and negative feedbacks equally. Given the different properties of positive samples and negative samples in RF, they should be treated differently. Considering this, we propose an orthogonal complement components analysis (OCCA) combined with SVM in this paper. We then generalize the OCCA to Hilbert space and define the kernel empirical OCCA (KEOCCA). Through experiments on a Corel photo database with 17,800 images, we demonstrate that the proposed method can significantly improve the performance of conventional SVM-based RF.

[1]  Thomas S. Huang,et al.  Small sample learning during multimedia retrieval using BiasMap , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Jing Peng,et al.  Multi-class relevance feedback content-based image retrieval , 2003, Comput. Vis. Image Underst..

[3]  Thomas S. Huang,et al.  Content-based image retrieval with relevance feedback in MARS , 1997, Proceedings of International Conference on Image Processing.

[4]  Qi Tian,et al.  Incorporate support vector machines to content-based image retrieval with relevance feedback , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[5]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[6]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[7]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[8]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[9]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Gio Wiederhold,et al.  Semantics-sensitive integrated matching for picture libraries and biomedical image databases , 2000 .

[12]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[13]  Bo Zhang,et al.  Support vector machine learning for image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[14]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[15]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[16]  Thomas S. Huang,et al.  Comparing discriminating transformations and SVM for learning during multimedia retrieval , 2001, MULTIMEDIA '01.

[17]  Jerome H. Friedman,et al.  Flexible Metric Nearest Neighbor Classification , 1994 .

[18]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[19]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[20]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..