Thomas Hofmann, Jan Puzicha and Joachim Buhmann,
Unsupervised Segmentation of Textured Images by Pairwise
Data Clustering,
In P. Delogne ed., IEEE International Conference on Image Processing (ICIP'96),
Lausanne, Switzerland, pp. 137-140, September 1996.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.