Combined color and texture segmentation by parametric distributional clustering

Unsupervised image segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametric distributional clustering (PDC) is presented as a novel approach to image segmentation based on color and texture clues. The objective function of the PDC model is derived from the recently proposed Information Bottleneck framework (Tishby et al., 1999), but it can equivalently be formulated in terms of a maximum likelihood solution. Its optimization is performed by deterministic annealing. Segmentation results are shown for natural wildlife imagery.