Set of texture similarity measures

This paper deals with a class of textures which can be represented by Markov Random Fields (MRF) model. It is well known that by changing the MRF parameters, extremely wide group of textures can be generated. However, it is not easy to model and classify a textured image, since there is no clear-cut mathematical definition of texture. Although, many classification methods exist in the literature, the success of the results heavily depends on the data type. Thus, appropriate measures which give visually meaningful representation of texture are highly desirable. In this study a new set of texture measures, namely, Mean Clique Length (MCL) and Clique Standard Deviation (CSD) is introduced. These measures are defined employing new concepts which agrees with the human visual system. The simulation experiments are performed on binary MRF texture alphabet to quantify the data by the MCL and CSD measures. Experimental results indicate that the introduced measures identify the visually similar textures much better than the mathematical distance measures.