A Multiscale Hierarchical Threshold-Based Completed Local Entropy Binary Pattern for Texture Classification

Abstract

Over the year, visual texture analysis has come to be recognized as one of the most important methods in the area of medical image analysis and understanding, face description and detection, and so on. The goal of texture descriptors is to capture the general characteristic of textures such as dependency as well as invariance properties. Among all the texture descriptors, the binary pattern family of algorithms achieves a great trade of representation efficiency and complexity. This work introduces an efficient discriminative texture descriptor for visual texture classification. Its main contribution is twofold: a multiscale thresholding framework based on hierarchical adaptive local partition to binary encoding and an efficient completed local entropy binary pattern (CLEBP) descriptor. The basic completed local entropy binary pattern is extended by multiscale thresholding framework with hierarchical thresholding to capture not only microstructure local patterns but also macrostructure texture information. Such extension improves the quality and discriminative factor of texture classification. Extensive experiments on three widely used benchmark texture databases (Outex, UIUC, and KTH-TIPS) proof the efficiency of the proposed visual texture descriptor and hierarchical thresholding strategy. Compared with some classical local binary pattern variants and many state-of-the-art methods, the proposed descriptor achieves competitive and superior texture classification performance. The results prove that the proposed method is a powerful and effective texture descriptor for visual texture classification.