Deterministic Annealing for Unsupervised Texture Segmentation

Abstract. 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 ...

• Type: Conference
• Year: 1997
• Where: EMMCVPR
• Authors: Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann
• Keywords: Computer Vision, EMMCVPR 1997, Pairwise Data Clustering, Texture Segmentation, Unsupervised Texture Segmentation