Texture characterization based on the Kolmogorov–Smirnov distance

The paper proposes the new numerical descriptor of the texture based on the Kolmogorov–Smirnov (KS) statistical distance. In this approach to feature generation we consider the distribution of the pixel intensity placed in equal circular distances from the central point. In this statistical analysis each pixel of the image takes the role of the central point and KS statistics is estimated for the whole image. We determine the KS distance of pixel intensity corresponding to the coaxial rings of the increasing distance from the center. The slope of the linear regression function applied for approximating the characteristics presenting KS distance versus the geometrical distance of these rings, forms the proposed statistical descriptor of the image. We show the application of this numerical description for recognition of the set of images of soil of different type and show that it behaves very well as the diagnostic feature, better than texture Haralick features.