Discrete Mixture Models for Unsupervised Image Segmentation
This paper introduces a novel statistical mixture model for probabilistic clustering of histogram data and, more generally, for the analysis of discrete co-occurrence data. Adopting the maximum likelihood framework, an alternating maximization algorithm is derived which is combined with annealing techniques to overcome the inherent locality of alternating optimization schemes. We demonstrate an application of this method to the unsupervised segmentation of textured images based on local empirical distributions of Gabor coefficients. In order to accelerate the optimization process an efficient multiscale formulation is utilized. We present benchmark results on a representative set of Brodatz mondrians and real-world images.