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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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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