Unsupervised segmentation of textured images by pairwise data clustering
A novel approach to unsupervised texture segmentation is presented 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 on a Gabor wavelet image representation. To efficiently solve the data clustering problem a deterministic annealing algorithm on the basis of a mean field approximation is derived. An application to collages of Brodatz-like microtexture is demonstrated. The adequacy of the proposed segmentation cost function is statistically validated. The deterministic annealing algorithm outperforms its stochastic variants in terms of quality and efficiency.