Co-occurrence shape descriptors applied to texture classification and segmentation

In order to segment images involving different micro/macro textures as Brodatz's textures or textile surfaces, we propose to use the co-occurrence feature in conjunction with a split and merge process. Our process can be viewed as a two-stage algorithm. In the first stage, a 'pixel-based' learning procedure characterizes all the studied textures. In the second stage a 'region-based' labeling procedure assigns pixels to different classes of textures. At each texture corresponds a co-occurrence matrix which can be described thanks to shape parameters or thanks to statistical parameters. We have used two measures from the co-occurrence matrix to discriminate each class of equivalent textures. The first one is a statistical measure which evaluates the local contrast. The second one is based on the convex hull of the co-occurrence matrix in order to describe its shape. The perimeter measure and the contrast measure have been selected not only because they are relevant texture descriptors but also because they are most robust to window size changes and to scale changes. From these features we have defined decision rules to assign each texture under study to its nearest class. We have especially used the Haussdorf metric to discriminate each texture from the other according to its shape.