A continuous probabilistic framework for image matching

Hayit Greenspan*, Jacob Goldberger, Lenny Ridel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper we describe a probabilistic image matching scheme in which the image representation is continuous and the similarity measure and distance computation are also defined in the continuous domain. Each image is first represented as a Gaussian mixture distribution and images are compared and matched via a probabilistic measure of similarity between distributions. A common probabilistic and continuous framework is applied to the representation as well as the matching process, ensuring an overall system that is theoretically appealing. Matching results are investigated and the application to an image retrieval system is demonstrated.

Original languageEnglish
Pages (from-to)384-406
Number of pages23
JournalComputer Vision and Image Understanding
Issue number3
StatePublished - Dec 2001


  • Gaussian mixture modeling
  • Image matching
  • Image representation
  • Kullback-Leibler distance
  • Probabilistic matching


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