Color Context Binary Pattern Using Progressive Bit Correction for Image Classification

SONG Tiecheng; FENG Jie; LI Shuang; ZHANG Tianqi

doi:10.1049/cje.2021.03.010

Volume 30 Issue 3

May 2021

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SONG Tiecheng, FENG Jie, LI Shuang, et al., “Color Context Binary Pattern Using Progressive Bit Correction for Image Classification,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 471-481, 2021, doi: 10.1049/cje.2021.03.010

Citation:

SONG Tiecheng, FENG Jie, LI Shuang, et al., “Color Context Binary Pattern Using Progressive Bit Correction for Image Classification,” Chinese Journal of Electronics, vol. 30, no. 3, pp. 471-481, 2021, doi: 10.1049/cje.2021.03.010

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Color Context Binary Pattern Using Progressive Bit Correction for Image Classification

doi: 10.1049/cje.2021.03.010

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.61702065) and Chongqing Research Program of Basic Research and Frontier Technology (No.cstc2018jcyjAX0033).

Received Date: 2020-08-06

Abstract

Abstract

Local binary pattern (LBP) is sensitive to noise. Noise-resistant LBP (NRLBP) addresses this problem by thresholding local neighboring pixels into three-valued states (i.e., 0, 1 and uncertain bits) and recovering uncertain bits via an error-correction mechanism. In this paper, we extend NRLBP to deal with color images and propose a robust color image descriptor called Color context binary pattern (CCBP). In CCBP, we employ scale context and neighbor context to progressively correct the encoded bits. First, we encode intra-channel local neighboring pixels into three-valued states in scale space and use majority voting to correct all states across scales. Then, we compute inter-channel color feature distances and correct the uncertain bits via neighboring bit propagation. Finally, we construct the image descriptor by concatenating all histograms based on the corrected binary codes. Experiments on four benchmark databases demonstrate the robustness of CCBP for color image classification under very low signal-to-noise ratio levels.
- Color,
- Image feature,
- Texture classification,
- Noise

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References(43)

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