Multiscale Annealing for Real{time Unsupervised Texture Segmentation Iai{tr{97{4

We derive real{time global optimizationmethods for several clustering optimization problems used in unsupervised texture segmentation. Speed is achieved by exploiting the topological relation of features to design a multiscale optimization technique, while accuracy and global optimization properties are gained using a deterministic annealing method. Coarse grained cost functions are derived for both central and sparse pairwise clustering, where the problem of coarsening sparse random graphs is solved by the concept of structured randomization. Annealing schedules and coarse{to{ ne optimization are tightly coupled by a statistical convergence criterion derived from computational learning theory. The algorithms are benchmarked on Brodatz{like micro{texture mixtures. Results are presented for an autonomous robotics application. J. Puzicha, J.M. Buhmann: Real{Time Texture Segmentation 1