Robust texture classification by subsets of local binary patterns

Recently, a nonparametric approach to texture analysis has been developed, in which the distributions of simple texture measures based on local binary patterns (LBP) are used for texture description. The basic LBP encodes 256 simple feature detectors in a single 3/spl times/3 operator. This paper shows that a properly selected subset of patterns encoded in LBP forms an efficient and robust texture description which can achieve better classification rates in comparison with the whole LBP histogram. Experiments on classification of textures from the Columbia-Utrecht (CURET) database demonstrate the robustness of the approach.