A generalized metric distance between hierarchically partitioned images

  • Authors:
  • Maude Manouvrier;Marta Rukoz;Geneviève Jomier

  • Affiliations:
  • Paris Dauphine University, France;Universidad Central de Venezuela - CCPD, Caracas, Venezuela;Paris Dauphine University, Paris Cedex, France

  • Venue:
  • MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
  • Year:
  • 2005

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Abstract

This article presents a generalized metric distance, called Δ-distance, between images represented by a tree structure resulting from a recursive image partition. This distance is used to perform content-based image retrieval queries in databases. Δ-distance allows to retrieve images globally similar to a query image. This distance takes into account the location of the image visual features. It can be performed using a multi-level filtering algorithm. Moreover, Δ-distance allows region-based queries. In this case, the resulting images contain quadrants similar to the quadrants selected by the user in the query image or contain quadrants similar to the entire query image. Because it is a generalized distance function, some particular cases of the Δ-distance appear in existing content-based image retrieval systems.