Performance comparison of different similarity models for CBIR with relevance feedback

Authors:
Daniel Heesch;Alexei Yavlinsky;Stefan Rüger
Affiliations:
Department of Computing, South Kensington Campus, Imperial College London, London, England;Department of Computing, South Kensington Campus, Imperial College London, London, England;Department of Computing, South Kensington Campus, Imperial College London, London, England
Venue:
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Year:
2003

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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.