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.