Abstract
Image databases will force us to rethink many of the concepts that led us so far. One of these is matching. We argue that the fundamental operation in a content-indexed image database should not be matching the query against the images in the database in search of a “target” image that best matches the query. The basic operation in query-by-content will be ranking portions of the database with respect to similarity with the query. What kind of similarity measure should be used is a problem we begin exploring in this paper. We let psychological experiments guide us in the quest for a good similarity measure, and devise a measure derived from a set-theoretic measure proposed in the psychological literature, modified by the introduction of fuzzy logic.
We report one experiment comparing this measure with other proposed in experimental psychology.
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© 1996 Springer-Verlag Berlin Heidelberg
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Santini, S., Jain, R. (1996). Similarity matching. In: Li, S.Z., Mital, D.P., Teoh, E.K., Wang, H. (eds) Recent Developments in Computer Vision. ACCV 1995. Lecture Notes in Computer Science, vol 1035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60793-5_110
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DOI: https://doi.org/10.1007/3-540-60793-5_110
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