Learning completed discriminative local features for texture classification

作者：

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.

摘要

•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.

论文关键词：Texture classification,Discriminative learning,Local binary patterns,Adaptive histogram accumulation

论文评审过程：Received 18 March 2016, Revised 15 February 2017, Accepted 16 February 2017, Available online 17 February 2017, Version of Record 21 February 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.021