Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern
SONG Tiecheng LUO Lin ZHANG Gang LUO Zhongtao ZHANG Tianqi
(Chongqing Key Laboratory of Signal and Information Processing (CQKLS&IP), Chongqing University of Posts and Telecommunications (CQUPT), Chongqing 400065, China)
Abstract：The traditional Local Binary Pattern (LBP) has limited feature discrimination and is sensitive to the noise. In order to alleviate these problems, this paper proposes a method to extract texture features based on pyramid decomposition and sectored local mean binary pattern. First, the pyramid decomposition is performed on the original image to obtain low-frequency and high-frequency (difference) images with different decomposition levels. To extract robust yet discriminative features, thresholding technique is further used to transform the high-frequency images into positive and negative high-frequency images. Then, based on local averaging operations, Sectored Local Mean Binary Pattern (SLMBP) is proposed and used to compute texture feature codes at different decomposition levels. Finally, the texture features are obtained by joint coding across frequency bands and by histogram weighting across decomposition levels. Experiments on three publicly available texture databases (Outex, Brodatz and UIUC) demonstrate that the proposed method can effectively improve the classification accuracy of texture images both in noise-free conditions and in the presence of different levels of Gaussian noise.
宋铁成,罗林, 张刚,罗忠涛,张天骐. 基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法[J]. 电子与信息学报, 2018, 40(6): 1301-1308.
SONG Tiecheng, LUO Lin, ZHANG Gang, LUO Zhongtao, ZHANG Tianqi. Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern. JEIT, 2018, 40(6): 1301-1308.
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