Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern

SONG Tiecheng; LUO Lin; ZHANG Gang; LUO Zhongtao; ZHANG Tianqi

doi:10.11999/JEIT170884

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 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[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884

Citation:

SONG Tiecheng, LUO Lin, ZHANG Gang, LUO Zhongtao, ZHANG Tianqi. Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884

Citation:

SONG Tiecheng, LUO Lin, ZHANG Gang, LUO Zhongtao, ZHANG Tianqi. Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884

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Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern

doi: 10.11999/JEIT170884

Funds:

The National Natural Science Foundation of China (61702065, 61671095), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009 CA2003)

Received Date: 2017-09-20
Rev Recd Date: 2018-02-01
Publish Date: 2018-06-19

Abstract

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.
- Texture classification,
- Feature extraction,
- Local Binary Pattern (LBP),
- Pyramid decomposition

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References(20)

References

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