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Similarity matching

  • Content-Based Retrieval
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Recent Developments in Computer Vision (ACCV 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1035))

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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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Correspondence to Simone Santini or Ramesh Jain .

Editor information

Stan Z. Li Dinesh P. Mital Eam Khwang Teoh Han Wang

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60793-9

  • Online ISBN: 978-3-540-49448-5

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