Similarity matching

  • Simone SantiniEmail author
  • Ramesh JainEmail author
Content-Based Retrieval
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1035)


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.


Face Image Image Database Salience Function Perceptual Distance Wide Mouth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  1. 1.Visual Computing LaboratoryUniversity of CaliforniaSan Diego

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