In this paper we present a new approach to material classification
under unknown viewpoint and illumination. Our texture model is based on
the statistical distribution of clustered filter responses.  However,
unlike previous 3D texton representations, we use rotationally
invariant filters and cluster in an extremely low dimensional space.

Having built a texton dictionary, we present a novel method of
classifying a single image without requiring any a priori knowledge
about the viewing or illumination conditions under which it was
photographed. We argue that using rotationally invariant filters while
clustering in such a low dimensional space improves classification
performance and demonstrate this claim with results on all 61 textures
in the Columbia-Utrecht database. We then proceed to show how texture models
can be further extended by compensating for viewpoint changes using
weak isotropy.

The new clustering and classification methods are compared to those of
Leung and Malik (ICCV 1999), Schmid (CVPR 2001) and Cula and Dana
(CVPR 2001), which are the current state-of-the-art approaches.