STATISTICAL GEOMETRICAL FEATURES FOR TEXTURE CLASSIFICATION

This paper proposes a novel set of 16 features based on the statistics of geometrical attributes of connected regions in a sequence of binar y images obtained from a texture image. Systematic comparison using al l the Brodatz textures shows that the new set achieves a higher correc t classification rate than the well-known Statistical Gray Level Depen dence Matrix method, the recently proposed Statistical Feature Matrix, and Liu's features. The deterioration in performance with the increas e in the number of textures in the set is less with the new SGF featur es than with the other methods, indicating that SGF is capable of hand ling a larger texture population, The new method's performance under a dditive noise is also shown to be the best of the four.