Local Binary Pattern histogram based Texton learning for texture classification

Abstract

Local Binary Pattern (LBP) and Texton are both widely used texture analysis techniques. In this paper we propose a patch-based texture classification method that takes advantage of both LBP and Texton. Unlike the traditional LBP methods that describe a texture with the occurrence of local binary patterns in the entire image, we compute the LBP histogram in a small region around each pixel to capture the local structure information. The texton learning method is then per- formed on these LBP histograms, resulting in a texture classification algorithm that outperforms the traditional LBP-based methods due to its preservation of local structure information. It also outperforms the traditional filtering-based texton methods due to its robustness to orientation and illumination. Experimental results on two benchmark databases validate the advantages of the proposed method.