Abstract
This paper reports on experimental results obtained from a comparative study of retrieval performance in content-based image retrieval. Two different learning techniques, k-Nearest Neighbours and support vector machines, both of which can be used to define the similarity between two images, are compared against the vector space model. For each technique, we determine both absolute retrieval performance as well as the relative increase in performance that can be achieved through relevance feedback.
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References
Y Chen, X Zhou, and T S Huang. One-class SVM for learning in image retrieval. In Proc IEEE Image processing 2001, 2001.
I J Cox, M L Miller, T P Minka, T V Papathomas, and P N Yianilos. The Bayesian image retrieval system, pichunter. IEEE Transactions on Image Processing, 9(1):20–38, 2000.
B V Dasarathy, editor. Nearest Neigbour (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, 1991.
D Heesch and S Rüger. Performance boosting with three mouse clicks — relevance feedback for CBIR. In Proceedings of the European Colloquium on IR Research 2003. LNCS, Springer, 2003.
T S Huang. Incorporating support vector machines in content-based image retrieval with relevance feedback. In Proc IEEE Image processing 2000, 2000.
B S Manjunath and J-R Ohm. Color and texture descriptors. IEEE Transactions on circuits and systems for video technology, 11:703–715, 2001.
C Meilhac and C Nastar. Relevance feedback and category search in image databases. In Proc. IEEE Int. Conf. Multimedia Comp. and Syst., pages 512–517, 1999.
T M Mitchell. Machine Learning. McGraw Hill, 1997.
H Müller, S Marchand-Maillet, and T Pun. The truth about Corel-evaluation in image retrieval. In Proceedings of CIVR, pages 38–49, 2002.
H Müller, W Müller, D M Squire, M.S Marchand-Maillet, and T Pun. Strategies for positive and negative relevance feedback in image retrieval. In Proceedings of the 15th International Conference on Pattern Recognition (ICPR 2000), IEEE, Barcelona, Spain, 2000.
M Pickering and S Rüger. Evaluation of key-frame based retrieval techniques for video. submitted to Elsevier Science and accepted for publication, 2003.
Y Rui and T S Huang. A novel relevance feedback technique in image retrieval. In ACM Multimedia (2), pages 67–70, 1999.
G Salton and M J Gill. Introduction to Modern Information Retrieval. McGraw-Hill Book Co., 1983.
B Schölkopf, J C Platt, J T Shawe, A J Smola, and R C Williamson. Estimation the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research, 1999.
V N Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995.
E M Voorhees and D Harman. Overview of the eigth Text REtrieval Conference (TREC-8). In Proc. TREC, pages 1–33 and A.17–A.18, 1999.
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Heesch, D., Yavlinsky, A., Rüger, S. (2003). Performance Comparison of Different Similarity Models for CBIR with Relevance Feedback. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_45
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DOI: https://doi.org/10.1007/3-540-45113-7_45
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