Abstract
Relevance Feedback (RF) is a useful technique in reducing semantic gap which is a bottleneck in Content-Based Image Retrieval (CBIR). One of the classical approaches to implement RF is feature re-weighting where weights in the similarity measure are modified using feedback samples as returned by the user. The main issues in RF are learning the system parameters from feedback samples and the high-dimensional feature space. We addressed the second problem in our previous work, here, we focus on the first problem. In this paper, we investigated different weight update schemes and compared the retrieval results. We proposed a new feature re-weighting method which we tested on three different image databases of size varying between 2000 and 8365, and having number of categories between 10 and 98. The experimental results with scope values of 20 and 100 demonstrated the superiority of our method in terms of retrieval accuracy.
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References
Zhang, H.: Relevance Feedback in Content-Based Image Retrieval. In: Feng, D.D., Siu, W.C., Zhang, H. (eds.) Multimedia Information Retrieval and Management-Technological Fundamentals and Applications, ch. 3. Springer, Germany (2003)
Rui, Y., Huang, T.S., Chang, S.: Image Retrieval: Current Techniques, Promising Directions and Open Issues. Journal of Visual Communication and Image Presentation 10(4) (April 1999)
Ortega-Binderberger, M., Mehrotra, S.: Relevance Feedback in Multimedia Databases. In: Handbook of Video Databases: Design and Applications, ch. 23, pp. 1–28. CRC Press, Boca Raton (2003)
Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval. IEEE Transactions on Circuits and Video Technology, Special issue on Segmentation, Description, and Retrieval of Video Content (September 1998)
Aksoy, S., Haralick, R.M.: A Weighted Distance Approach to Relevance Feedback. In: International Conference on Pattern Recognition, Barcelona Spain (September 2000)
Hore, E.S., Ray, S.: A Sum-result Indexing Algorithm for Feature Combining in Content-Based Image Retrieval. In: Proceedings of the Fourth IASTED International Conference Signal and Image Processing, Hawaii, USA (August 2002), pp. 283–287
Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying Databases through Multiple Examples. In: Proceedings 24th International Conference on Very Large Data Bases (VLDB) (1998)
Wu, Y., Zhang, A.: A Feature Re-weighting Approach for Relevance Feedback in Image Retrieval. Special issue on Segmentation, Description, and Retrieval of Video Content, Rochester NewYork (September 2002)
Das, G., Ray, S.: A Compact Feature Representation and Image Indexing in Content-Based Image Retrieval. In: Proceedings of Image and Vision Computing New Zealand 2005 Conference (IVCNZ 2005), Dunedin, New Zealand, November 28-29, pp. 387–391 (2005)
Huang, J.: Color-spatial Image Indexing and Applications, Ph.D Dissertation, Cornell University (1998)
Shim, S., Choi, T.: Image Indexing by Modified Color Co-occurrence Matrix. In: International Conference on Image Processing (September 2003)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6), 610–621 (1973)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2000)
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Das, G., Ray, S., Wilson, C. (2006). Feature Re-weighting in Content-Based Image Retrieval. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_20
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DOI: https://doi.org/10.1007/11788034_20
Publisher Name: Springer, Berlin, Heidelberg
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