A graph based approach for color texture classification in HSV color space

Color and texture have been proven to be very discriminant attributes in image analysis across many works. This paper proposes a color texture analysis method based on the graph theory, in which we convert the texture in question into an undirected weighted graph and explore the shortest paths between four pairs of pixels according to different scales and orientations of the image. Basically, we extend two previously introduced approaches that consider the RGB color space, to textures in the HSV color space taking into account the nature of correlations between its color channels. In order to evaluate its performance, we applied this procedure to USPTex textures dataset. The best classification results using the standard parameters of the method are 92.25%, 91.75% and 85.72% of Accuracy (percentage of samples correctly classified). Which proves the efficiency of the proposed method compared to the results achieved by traditional methods found in literature.