Abstract

We propose a novel approach to unsupervised texture segmentation, which is formulated as a combinatorial optimization problem known as pairwise data clustering with a sparse neighborhood structure. Pairwise dissimilarities between texture blocks are measured in terms of distribution differences of multi-resolution features. The feature vectors are based an a Gabor wavelet image representation. To efficiently solve the data clustering problem a deterministic annealing algorithm based on a meanfield approximation is derived. An application to Brodatz-like microtexture mixtures is shown. We statistically adress the questions of adequacy of the proposed cost function and the quality of the deterministic annealing algorithm compared with its stochastic variants.