We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multi scale Gabor lter image representation. We discuss and compare a class of clustering objective functions which is systematically derived from invariance principles. As a general optimization framework we propose deterministic annealing based on a mean eld approximation. The canonical way to derive clustering algorithms within this framework as well as an e cient implementation of mean eld annealing and the closely related Gibbs sampler are presented. We apply both annealing variants to Brodatz like micro texture mixtures and real word images.
A very different approach is proposed by REF that also performs unsupervised texture segmentation, but does it by relying on statistical tests as a measure of homogeneity.
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Trans. Pattern Anal. Mach. Intell.
Computer Science