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. [8]), 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.