Learning completed discriminative local features for texture classification

Zhong Zhang, Shuang Liu, Xing Mei, Baihua Xiao, Liang Zheng

Abstract: Highlights•We propose a novel feature representation method, namely Completed Discriminative Local Features (CDLF), for texture classification.•The CDLF learn transformation matrices for texture images that maximize the mutual information between the local features and their category labels.•We propose an adaptive histogram accumulation (AHA) algorithm, which leverages the local contrast characteristic in the process of histogram accumulation.•The CDLF achieves higher accuracy than state-of-the-art methods on three databases.