Unsupervised Texture Segmentationon the Basis of Scale Space
A novel approach to unsupervised texture segmentation is presented, which is formulated as a combinatorial optimization problem known as sparse pairwise data clustering. Pairwise dissimi-larities between texture blocks are measured by scale space features, i.e., multi-resolution edges. These scale space features are computed by a Gabor lter bank tuned to spatial frequencies. To solve the data clustering problem a deterministic annealing technique is applied. This approach is examined from the viewpoint of scale space theory. Based on a meanneld approximation an eecient algorithm is derived. We present an application of the proposed algorithm to Brodatz{ like microtexture collages.