Texture Classification Using the Belief Net of a Segmentation Tree

This study presents a statistical approach to texture classification from a single image obtained under unknownviewpoint and illumination. Unlike in prior work, in which texture primitives (textons) are defined in a filter-responsespace and texture classes modeled by frequency histograms of these textons, we seek to extract and model geometric and photometric properties of image regions defining the texture. To this end, texture images are first segmented bya multiscale segmentation algorithm and a universal set of texture primitives is specified over all texture classes in the domain of region geometric and photometric properties. Then, for each class, a Tree-Structured Belief Network (TSBN) is learned, where nodes represent the corresponding image regions and edges, their statistical dependecies. A given unknown texture is classified with respect to themaximum posterior distribution of the TSBN. Experimental results on the benchmark CUReT database demonstrate that our approach outperforms the state-of-the-artmethods.