Deterministic Annealing for Unsupervised Texture Segmentation
In this paper a rigorous mathematical framework of deterministic annealing and mean-field approximation is presented for a general class of partitioning, clustering and segmentation problems. We describe the canonical way to derive efficient optimization heuristics, which have a broad range of possible applications in computer vision, pattern recognition and data analysis. In addition, we prove novel convergence results. As a major practical application we present a new approach to the problem of unsupervised texture segmentation which relies on statistical tests as a measure of homogeneity. More specifically, this results in a formulation of texture segmentation as a pairwise data clustering problem with a sparse neighborhood structure. We discuss and compare different clustering objective functions, which are systematically derived from invariance principles. The quality of the novel algorithms is empirically evaluated on a large database of Brodatz-like micro-texture mixtures and on a representative set of real-word images.