We investigate texture classification from single images obtained
under unknown viewpoint and illumination.  A statistical approach is
developed where textures are modelled by the joint probability
distribution of filter responses. This distribution is represented by
the frequency histogram of filter response cluster centres
(textons). Recognition proceeds from single, uncalibrated images and
the novelty here is that rotationally invariant filters are used and
the filter response space is low dimensional.

Classification performance is compared with the filter banks and
methods of Leung and Malik [IJCV 2001], Schmid [CVPR 2001] and Cula
and Dana [IJCV 2004] and it is demonstrated that superior performance
is achieved here. Classification results are presented for all 61
materials in the Columbia-Utrecht texture database.

We also discuss the effects of various parameters on our
classification algorithm -- such as the choice of filter bank and
rotational invariance, the size of the texton dictionary as well as
the number of training images used. Finally, we present a method of
reliably measuring relative orientation co-occurrence statistics in a
rotationally invariant manner, and discuss whether incorporating such
information can enhance the classifier's performance.