Texture classification using discrete Tchebichef moments

Abstract

In this paper, an innovative method to characterize texture images based on discrete Tchebichef moments (DTM) is presented. A global signature pattern is derived from the moment matrix by taking into account both the magnitude of the moments and their order. The performance of our method for several texture classification problems was compared with that achieved through other standard approaches. These include Haralick’s gray level co-occurrence matrices (GLCM), Gabor filters (GF) and local binary patterns (LBP). An extensive texture classification study was carried out by selecting images with different contents from Brodatz, Outex and VisTex databases. The results show that the proposed method is able to capture the essential information about texture showing comparable or even higher performance than conventional procedures. Thus, it can be considered as an effective and competitive technique for texture characterization.