Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. We propose an integrated approach for image segmentation based on a generative clustering model combined with coarse shape information and robust parameter estimation. The sensitivity of segmentation solutions to image variations is measured by image resampling. Shape information is included in the inference process to guide ambiguous groupings of color and texture features. Shape and similarity-based grouping information is combined into a semantic likelihood map in the framework of Bayesian statistics. Experimental evidence shows that semantically meaningful segments are inferred even when image data alone gives rise to ambiguous segmentations.

Pairwise data clustering is a well founded grouping technique based on relational data of objects which has a widespread application domain. However, its applicability suffers from the disadvantageous fact that N objects give rise to N(N-1)/2 relations. To cure this unfavorable scaling, techniques to sparsely sample the relations have been developed. Yet a randomly chosen subset of the data might not grasp the structure of the complete data set. To overcome this deficit, we use active learning methods from the field of statistical decision theory. Extending existing approaches we present an algorithm for actively learning hierarchical group structures based on mean field annealing optimization.