Abstract
We analyze the special structure of the relevance feedback learning problem, focusing particularly on the effects of image selection by partial relevance on the clustering behavior of feedback examples. We propose a scheme, aspect-based relevance learning, which guarantees that feedback on feature values is accepted only once evidential support that the feedback was intended by the user is sufficiently strong. The scheme additionally allows for natural simulation of the relevance feedback process. By means of simulation we analyze retrieval performance, search regularity and sensitivity to feature errors.
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Huiskes, M.J. (2005). Aspect-Based Relevance Learning for Image Retrieval. In: Leow, WK., Lew, M.S., Chua, TS., Ma, WY., Chaisorn, L., Bakker, E.M. (eds) Image and Video Retrieval. CIVR 2005. Lecture Notes in Computer Science, vol 3568. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11526346_67
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DOI: https://doi.org/10.1007/11526346_67
Publisher Name: Springer, Berlin, Heidelberg
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