Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations

Shiri Gordon, Hayit Greenspan, Jacob Goldberger

Research output: Contribution to conferencePaperpeer-review

Abstract

In this paper we present a method for unsupervised clustering of image databases. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Image archives are clustered such that the mutual information between the clusters and the image content is maximally preserved. The IB principle is applied to both discrete and continuous image representations, using discrete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities, respectively. Experimental results demonstrate the performance of the proposed method for image clustering on a large image database. Several clustering algorithms derived from the IB principle are explored and compared.

Original languageEnglish
Pages370-377
Number of pages8
DOIs
StatePublished - 2003
EventProceedings: Ninth IEEE International Conference on Computer Vision - Nice, France
Duration: 13 Oct 200316 Oct 2003

Conference

ConferenceProceedings: Ninth IEEE International Conference on Computer Vision
Country/TerritoryFrance
CityNice
Period13/10/0316/10/03

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