A comparative analysis of local binary pattern texture classification

Abstract

Texture recognition is an important aspect of many computer vision applications. Local binary pattern (LBP) based texture algorithms have gained significant popularity in recent years and have been shown to be useful for a variety of tasks. While over the years a variety of LBP algorithms have been introduced in the literature, what is missing is a comprehensive evaluation of their performance. In this paper, we fill this gap and benchmark 37 texture descriptors based on 15 LBP variants for texture classification against common standard datasets of textures including those captured at different rotation angles and under different illumination conditions. Overall, LBP variance (LBPV) is found to give the best texture classification performance.