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

The proposed method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle, which is applied to both discrete and continuous image representations, using discrete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities.

Abstract

In this paper we present a method for unsupervised image clustering. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Images 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 dis-crete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities, respec-tively. Experimental results demonstrate the performance of the proposed method for image clustering on a large im-age database. Several clustering algorithms derived from the IB principle are explored and compared.