A Completed Gaussian Extended Binary Pattern for Texture Image Classification

TL;DR: A completed Gaussian extended binary pattern is proposed for texture image classification, which achieves state-of-the-art performance.

Abstract: Texture image classification is a fundamental and challenging visual task and has wide range of applications. Binary pattern methods play an important role in texture feature extraction due to its ease of implementation and promising performance. To extract completed texture feature representation and improve the classification performance, this paper proposes a completed gaussian extend binary pattern for texture image classification. First, instead of the original pixel value, this paper uses the mean of local range to encode the binary pattern. Second, this paper introduces a novel gaussian sign pattern to fully represent the macro texture structure. Third, to achieve completed texture feature description, this paper presents a completed gaussian extended binary pattern, which combines the novel gaussian sign pattern, the local sign and magnitude pattern extracted from mean-processing texture image. To validate the effectiveness of the proposed completed gaussian extend binary pattern, experimental evaluations are conducted on three test subsets from Outex database. The evaluation results show that the proposed completed gaussian extended binary pattern achieves the state-pf-the-art classification performance.