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.


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    Title :

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


    Contributors:
    Gordon, (author) / Greenspan, (author) / Goldberger, (author)


    Publication date :

    2003-01-01


    Size :

    535257 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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