Color texture classification based on gravitational collapse

摘要

Texture and color are essential attributes to be analyzed for any robust computer vision system. This paper presents a novel method to analyze color-texture images, based on representing states of a simplified gravitational collapse from each image color channel and extracting information from each state using the Bouligand-Minkowski fractal dimension and the lacunarity method. In this approach, we obtained the best classification results when the images of each channel evolved in times t=l1,5,10,15r, each time representing a state, using radius r=l3,4,5,6r for the Bouligand-Minkowski method and box size l=l2,3,4,5,6r for the lacunarity method. The best classification results were 99.37% and 96.57% of success rate (percentage of samples correctly classified) for VisTex and USPTex databases, respectively. These results prove that the proposed approach opens a promising source of research in color texture analysis still to be explored.