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Published Articles >> Table of Contents >> Abstract
15th International Conference on Pattern Recognition (ICPR'00) - Volume 1
p. 1038
A Unifying View of Image Similarity
Nuno Vasconcelos, Massachusetts Institute of Technology
Andrew Lippman, Massachusetts Institute of Technology
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.905271
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| Abstract |
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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 result from explicit minimization of error probability.
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Citation:
Nuno Vasconcelos, Andrew Lippman,
"A Unifying View of Image Similarity,"
icpr,
p. 1038,
15th International Conference on Pattern Recognition (ICPR'00) - Volume 1,
2000
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