Co-occurrence features of multi-scale directional filter bank for texture characterization

In this paper, we propose to use co-occurrence features computed from multi-scale directional filter bank (MDFB) for texture characterization. As the filter band coefficients are localized frequency components, features from co-occurrence matrices of filter bands can characterize structures of textures by describing correlation among coefficients. Our experiments show that the co-occurrence features outperform energy features considerably in texture retrieval. In particular, they significantly improve the retrieval rate for textures with weak directionality and periodicity while still maintains a high retrieval rate for regular textures as the energy features