CFS: an effective statistical texture descriptor

Coarseness is an effective description for texture analysis and many approaches capture this property of texture in different methods. In this research, we propose a new texture descriptor (CFS) that works based on coarseness and the fineness similarity score. For tuning of its configuration, we collect coarse and fine textures based on human visual perception at first. After that, we relabel the "coarse" and "fine" categories of the gathered textures during the configuration of the operator in a proposed framework. We concatenated the features of two pyramid representations with the CFS information of the original texture (PCFS). In addition, we combine the PCFS information with our last proposed feature selection approach to improve the efficiency of the CFS. We evaluated the proposed feature extraction method with classification of one of the well-known data set, Outex. Experimental results depict satisfactory performance of the CFS with very low-dimensional feature length.