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<p><b>Abstract</b>—Many multimedia content-based retrieval systems allow query formulation with user setting of relative importance of features (e.g., color, texture, shape, etc) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. In this paper, we present a neural network-based learning algorithm for adapting similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (<it>misranks</it>) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.</p>
Content-based retrieval, image retrieval, multimedia databases, learning, ranking, similarity matching, relevance feedback.

J. K. Wu, D. Narasimhalu, J. Lim and S. Singh, "Learning Similarity Matching in Multimedia Content-Based Retrieval," in IEEE Transactions on Knowledge & Data Engineering, vol. 13, no. , pp. 846-850, 2001.
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