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 binary images obtained from a texture image. Systematic comparison using all the Brodatz textures shows that the new set achieves a higher correct classification rate than the well-known Statistical Gray Level Dependence Matrix method, the recently proposed Statistical Feature Matrix, and Liu’s features. The deterioration in performance with the increase in the number of textures in the set is less with the new SGF features than with the other methods, indicating that SGF is capable of handling a larger texture population. The new method’s performance under additive noise is also shown to be the best of the four. Texture analvsis Feature extraction Statistical features Geometrical features Additive noise