Upper and lower volumetric fractal descriptors for texture classification

In this work, we propose a novel set of fractal descriptors extracted from gray-level texture images using Bouligand-Minkowski method. To accomplish that, we first convert the texture image into a surface, where the z-axis is the intensity value associated to a pixel in the image. Then, we use a sphere of radius r to dilate this surface. However, instead of using only the influence volume information to extract the descriptors, we considered that the arrangement of the pixels in the texture interferes in the dilation process differently in regions of concavity and convexity of the texture. Thus, we separate these descriptors in two sets: upper and lower volumetric fractal descriptors. Classification results of the descriptors show that they presents a great discrimination power as they overcome traditional methods found in literature in three different benchmark databases, proving to be an efficient tool for texture analysis.