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2000
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A Unifying View of Image Similarity

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A Unifying View of Image Similarity
We study solutions to the problem of evaluating image similarity in the context of content-based image retrieval (CBIR). Retrieval is formulated as a classification problem, where the goal is to minimize probability of retrieval error. It is shown that this formulation establishes a common ground for comparing similarity functions, exposes assumptions hidden behind most of the ones in common use, enables a critical analysis of their relative merits, and determines the retrieval scenarios for which each may be most suited. We conclude that most of the current similarity functions are sub-optimal special cases of the Bayesian criteria that results from explicit minimization of error probability.
Nuno Vasconcelos, Andrew Lippman
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where ICPR
Authors Nuno Vasconcelos, Andrew Lippman
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