Abstract

Unsupervised Image Segmentation is one of the central issues in Computer Vision. From the viewpoint of exploratory data analysis, segmentation can be formulated as a clustering problem in which pix- els or small image patches are grouped together based on local feature information. In this contribution, parametrical distributional clustering (PDC) is presented as a novel approach to image segmentation. In con- trast to noise sensitive point measurements, local distributions of image features provide a statistically robust description of the local image prop- erties. The segmentation technique is formulated as a generative model in the maximum likelihood framework. Moreover, there exists an insightful connection to the novel information theoretic concept of the Informa- tion Bottleneck (Tishby et al. [17]), which emphasizes the compromise between efficient coding of an image and preservation of characteristic information in the measured feature distributions. The search for good grouping solutions is posed as an optimization problem, which is solved by deterministic annealing techniques. In order to further increase the computational eficiency of the resulting segmentation algorithm, a multi-scale optimization scheme is developed. Finally, the performance of the novel model is demonstrated by segmen- tation of color images from the Corel data base.