Rotation-invariant colour texture classification through multilayer CCR

Abstract

The Coordinated Clusters Representation (CCR) is a texture descriptor based on the probability of occurrence of elementary binary patterns (texels) defined over a square window. The CCR was originally proposed for binary textures, and it was later extended to grayscale texture images through global image thresholding. The required global binarization is a critical point of the method, since this preprocessing stage can wipe out textural information. Another important drawback of the original CCR model is its sensitivity against rotation. In this paper we present a rotation-invariant CCR-based model for colour textures which yields a twofold improvement over the grayscale CCR: first, the use of rotation-invariant texels makes the model insensitive against rotation; secondly, the new texture model benefits from colour information and does not need global thresholding. The basic idea of the method is to describe the textural and colour content of an image by splitting the original colour image into a stack of binary images, each one representing a colour of a predefined palette. The binary layers are characterized by the probability of occurrence of rotation-invariant texels, and the overall feature vector is obtained by concatenating the histograms computed for each layer. In order to quantitatively assess our approach, we performed experiments over two datasets of colour texture images using five different colour spaces. The obtained results show robust invariance against rotation and a marked increase in classification accuracy with respect to grayscale versions of CCR and LBP.