Unsupervised image clustering using the information bottleneck method

Jacob Goldberger, Hayit Greenspan, Shiri Gordon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations


A new method for unsupervised image category clustering is presented, based on a continuous version of a recently introduced information theoretic principle, the information bottleneck (IB). The clustering method is based on hierarchical grouping: Utilizing a Gaussian mixture model, each image in a given archive is first represented as a set of coherent regions in a selected feature space. Images are next grouped such that the mutual information between the clusters and the image content is maximally preserved. The appropriate number of clusters can be determined directly from the IB principle. Experimental results demonstrate the performance of the proposed clustering method on a real image database.

Original languageEnglish
Title of host publicationPattern Recognition - 24th DAGM Symposium, Proceedings
EditorsLuc Van Gool, Luc Van Gool, Luc Van Gool
PublisherSpringer Verlag
Number of pages8
ISBN (Print)354044209X, 9783540442097
StatePublished - 1 Jan 2002
Externally publishedYes
Event24th Symposium of the German Pattern Recognition Association, DAGM 2002 - Zurich, Switzerland
Duration: 16 Sep 200218 Sep 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th Symposium of the German Pattern Recognition Association, DAGM 2002


  • Gaussian mixture modeling
  • Image categories
  • Image grouping
  • Information bottleneck
  • Kullback-leibler distance
  • Unsupervised clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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