Abstract
Relevance feedback (RF) is a mechanism introduced earlier to exploit a user’s perceptual feedback in image retrieval. It refines a query by using the relevance information from the user to improve subsequent retrieval. However, the user’s feedback information is generally lost after a search session terminates. In this paper, we propose an enhanced version of RF, which is designed to accumulate human perceptual responses over time through relevance feedback and to dynamically combine the accumulated high-level relevance information with low-level features to further improve the retrieval effectiveness. Experimental results are presented to demonstrate the potential of the proposed method.
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© 2004 Springer-Verlag Berlin Heidelberg
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Oh, S., Chung, M.G., Sull, S. (2004). Relevance Feedback Reinforced with Semantics Accumulation. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds) Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, vol 3115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27814-6_53
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DOI: https://doi.org/10.1007/978-3-540-27814-6_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22539-3
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