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Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval
July 2006 (vol. 28 no. 7)
pp. 1088-1099
Xuelong Li, IEEE
Xindong Wu, IEEE
Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance.

[1] D. Bahler and L. Navarro, “Methods for Combining Heterogeneous Sets of Classifiers,” Proc. 17th Nat'l Conf. Am. Assoc. for Artificial Intelligence, 2000.
[2] L. Breiman, “Bagging Predictors,” Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
[3] J.C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
[4] T. Chang and C. Kuo, “Texture Analysis and Classification with Tree-Structured Wavelet Transform,” IEEE Trans. Image Processing, vol. 2, no. 4, pp. 429-441, 1993.
[5] Y. Chen, X. Zhou, and T.S. Huang, “One-Class SVM for Learning in Image Retrieval,” Proc. IEEE Int'l Conf. Image Processing, pp. 815-818, 2001.
[6] G. Guo, A.K. Jain, W. Ma, and H. Zhang, “Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback,” IEEE Trans. Neural Networks, vol. 12, no. 4, pp. 811-820, 2002.
[7] T.K. Ho, “The Random Subspace Method for Constructing Decision Forests,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, Aug. 1998.
[8] P. Hong, Q. Tian, and T.S. Huang, “Incorporate Support Vector Machines to Content-Based Image Retrieval with Relevant Feedback,” Proc. IEEE Int'l Conf. Image Processing, pp. 750-753, 2000.
[9] A. Jain and A. Vailaya, “Image Retrieval Using Color and Shape,” Pattern Recognition, vol. 29, no. 8, pp. 1233-1244, 1996.
[10] M. Jordan and R. Jacobs, “Hierarchical Mixtures of Experts and the EM Algorithm,” Int'l. J. Neural Computation, vol. 6, no. 5, pp. 181-214, 1994.
[11] D. Kim and C. Kim, “Forecasting Time Series with Genetic Fuzzy Predictor Ensemble,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 523-535, 1997.
[12] J. Kittler, M. Hatef, P.W. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[13] B. Manjunath and W. Ma, “Texture Features for Browsing and Retrieval of Image Data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-842, Aug. 1996.
[14] B. Manjunath, J. Ohm, V. Vasudevan, and A. Yamada, “Color and Texture Descriptors,” IEEE Trans. Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 703-715, 2001.
[15] J. Mao and A. Jain, “Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models,” Pattern Recognition, vol. 25, no. 2, pp. 173-188, 1992.
[16] W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos, and G. Taubino, “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape,” Proc. SPIE Storage and Retrieval for Images and Video Databases, pp. 173-181, 1993.
[17] G. Pass, R. Zabih, and J. Miller, “Comparing Images Using Color Coherence Vectors,” Proc. ACM Int'l Conf. Multimedia, pp. 65-73, 1996.
[18] J. Peng, “MultiClass Relevance Feedback Content-Based Image Retrieval,” Computer Vision and Image Understanding, vol. 90, no. 1, pp. 42-67, 2003.
[19] J. Platt, “Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods,” Proc. Advances in Large Margin Classifiers, pp. 61-74, 2000.
[20] G. Ratsch, S. Mika, B. Scholkopf, and K.R. Muller, “Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1184-1199, Sept. 2002.
[21] Y. Rui, T.S. Huang, and S. Mehrotra, ”Content-Based Image Retrieval with Relevance Feedback in MARS,” Proc. IEEE Int'l Conf. Image Processing, vol. 2, pp. 815-818, 1997.
[22] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
[23] M. Swain and D. Ballard, “Color Indexing,” Int'l J. Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[24] H. Tamura, S. Mori, and T. Yamawaki, “Texture Features Corresponding to Visual Perception,” IEEE Trans. Systems, Man, and Cybernetics, vol. 8, no. 6, pp. 460-473, 1978.
[25] D. Tao and X. Tang, “Random Sampling Based SVM for Relevance Feedback Image Retrieval,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 647-652, 2004.
[26] K. Tieu and P. Viola, “Boosting Image Retrieval,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 228-235, 2001.
[27] S. Tong and E. Chang, “Support Vector Machine Active Learning for Image Retrieval,” Proc. ACM Int'l Conf. Multimedia, pp. 107-118, 2001.
[28] V. Tresp and M. Taniguchi, “Combining Estimators Using Non-Constant Weighting Functions,” Advances in Neural Information Processing Systems, pp. 419-426, 1995.
[29] V. Vapnik, The Nature of Statistical Learning Theory. Springer-Verlag, 1995.
[30] J. Wang, J. Li, and G. Wiederhold, “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, Sept. 2001.
[31] L. Zhang, F. Lin, and B. Zhang, “Support Vector Machine Learning for Image Retrieval,” Proc. IEEE Int'l Conf. Image Processing, pp. 721-724, 2001.
[32] X. Zhou and T.S. Huang, “Small Sample Learning During Multimedia Retrieval Using Biasmap,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 11-17, 2001.
[33] X. Zhou and T.S. Huang, “Relevance Feedback for Image Retrieval: A Comprehensive Review,” ACM Multimedia Systems J., vol. 8, no. 6, pp. 536-544, 2003.
[34] http://www.ece.osu.edu/~majosu_svm/, 2002.

Index Terms:
Classifier committee learning, content-based image retrieval, relevance feedback, asymmetric bagging, random subspace, support vector machines.
Citation:
Dacheng Tao, Xiaoou Tang, Xuelong Li, Xindong Wu, "Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1088-1099, July 2006, doi:10.1109/TPAMI.2006.134
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