Abstract
In this paper, we present a novel relevance feedback algorithm for content-based image retrieval using the PSVM (Proximal Support Vector Machine). The PSVM seeks to find the optimal separating hyperplane by “regularized least squares”. The obtained hyperplane comprises the positive and negative “proximal planes”. We interpret the proximal vectors on the proximal planes as the representatives among training samples, and propose to use the distance from the positive proximal plane as a measure of image dissimilarity. In order to reduce computational time for relevance feedback, we introduce the “expanded sets” derived from the pre-computed dissimilarity matrix, and apply the feedback algorithm to these expanded sets rather than the entire image database, while preserving the comparable precision rate. We demonstrate the efficacy of the proposed scheme using unconstrained image databases that were obtained from the Web.
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References
Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. In: 7th WWW Conference on Computer Networks and ISDN Systems, April 1998, vol. 30, pp. 107–117 (1998)
Chen, Y., Wang, J.Z., Krovetz, R.: An unsupervised learning approach to content-based image retrieval. In: Seventh International Symposium on Signal Processing and its Applications (ISSPA 2003), Paris, vol. 1, pp. 197–200 (2003)
Chen, Y., Zhou, X.S., Huang, T.S.: One-class svm for learning in image retrieval. IEEE Conference on Image Processing 1, 34–37 (2001)
Choi, Y., Kim, D., Krishnapuram, R.: Relevance feedback for contentbased image retrieval using the Choquet integral. In: IEEE International Conference on Multimedia and Expo., pp. 1207–1210 (2000)
Cortes, C., Vapnik, V.: Support vector network. Machine learning 20, 273–297 (1995)
Enser, P., Sandom, C.: Towards a comprehensive survey of the semantic gap in visual image retrieval. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 291–299. Springer, Heidelberg (2003)
Fung, G., Mangasarian, O.L.: Proximal support vector machine classifiers. Knowledge Discovery and Data Mining, 77–86 (2001)
Guo, G.D., Jain, A.K., Ma, W.Y., Zhang, H.J.: Learning similarity measure for natural image retrieval with relevance feedback. IEEE Transactions on Neural Networks 13(4), 811–820 (2002)
Guo, G.D., Zhang, H.J., Li, S.Z.: Distance from boundary as a metric for texture image retrieval. In: International Conference on Acoustics, Speech, and Signal Processing, May 2001, vol. 3 (2001)
Hong, P., Tian, Q., Huang, T.S.: Incorporate support vector machines to content-based image retrieval with relevant feedback. In: IEEE International Conference on Image Processing, September 2000, vol. 3, pp. 750–753 (2000)
Heesch, D., Yavlinsky, A., Ruger, S.: Performance comparison of different similarity models for CBIR with relevance feedback. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 456–466. Springer, Heidelberg (2003)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: 9th Annual ACMSIAM Symposium on Discrete Algorithms, January 1998, pp. 668–677 (1998)
Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 716–719 (2001)
Platt, J.C.: Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, April 21 (1998)
Tian, Q., Hong, P., Huang, T.S.: Update relevant image weights for content-based image retrieval using support vector machines. In: IEEE International Conference on Multimedia and Expo, June 2000, vol. 2, pp. 1199–1202 (2000)
Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: ACM International Conference on Multimedia, Ottawa, October 2001, pp. 107–118 (2001)
Zhang, L., Lin, F., Zhang, B.: Support vector machine learning for image retrieval. In: IEEE International Conference on Image Processing, October 2001, vol. 2, pp. 721–724 (2001)
Zhou, X.S., Haung, T.S.: Comparing discriminate transformations and SVM for learning during multimedia retrieval. In: ACM Multimedia 2001, Ottawa, Ontario, Canada, September 30-October 5 (2001)
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Choi, Y., Noh, J. (2004). Relevance Feedback for Content-Based Image Retrieval Using Proximal Support Vector Machine. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24709-8_99
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DOI: https://doi.org/10.1007/978-3-540-24709-8_99
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