Color Textons for Texture Recognition

Texton models have proven to be very discriminative for the recognition of grayvalue images taken from rough textures. To further improve the discriminative power of the distinctive texton models of Varma and Zisserman (VZ model) (IJCV, vol. 62(1), pp. 61-81, 2005), we propose two schemes to exploit color information. First, we incorporate color information directly at the texton level, and apply color invariants to deal with straightforward illumination effects as local intensity, shading and shadow. But, the learning of representatives of the spatial structure and colors of textures may be hampered by the wide variety of apparent structure-color combinations. Therefore, our second contribution is an alternative approach, where we weight grayvalue-based textons with color information in a post-processing step, leaving the original VZ algorithm intact. We demonstrate that the color-weighted textons outperform the VZ textons as well as the color invariant textons. The color-weighted textons are specifically more discriminative than grayvalue-based textons when the size of the example image set is reduced. When using 2 example images only, recognition performance is 85.6%, which is an improvement over grayvaluebased textons of 10%. Hence, incorporating color in textons facilitates the learning of textons.