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A comprehensively improved local binary pattern framework for texture classification

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Abstract

Local binary pattern (LBP) has been widely studied for its remarkable texture feature extraction capabilities. A large number of LBP variants have been proposed. Most of these variants still use the traditional LBPriu2 mapping method to achieve the purpose of dimensionality reduction or improvement of classification accuracy. In fact, LBPriu2 loses much texture information. Inspired by the idea of utilizing complementary information and multi-operator fusion, a mapping method based on triple complementary information (LBPOUS) is proposed in this paper. LBPOUS can further use complementary texture information to provide an alternative mapping solution for LBP variants, and it can be applied to any LBP variants that require mapping. Furthermore, classification accuracy, noise robustness, and computational complexity have always been the major problems for LBP to balance. This paper proposes a comprehensively improved local binary pattern framework (CILBPF). Firstly, the alpha-trimmed mean filter (ATMF) is improved to obtain well-rounded noise robustness. Secondly, LBPOUS is applied to LBP variants to retain richer texture information. Finally, the zero elimination (ZE) is improved to ensure that the feature dimensions are maintained at a relatively low level. Experiments are conducted on five representative public texture datasets, Outex, CUReT, UIUC, UMD, and ALOT. The results show that the proposed CILBPF can effectively improve the classification performance of LBP variants by adopting LBPOUS while ensuring computational complexity. Meanwhile, better handling of Gaussian noise and salt-and-pepper noise is also acquired. It is worth mentioning that the proposed framework also exhibits excellent noise robustness against mixed noise.

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Data availability

The data of this study are available from the corresponding author upon reasonable request.

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Funding

The research leading to these results received funding from Supporting enterprise technology innovation and development projects of Hubei Province under Grant Agreement No.2021BAB040.

Author information

Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Yuyan Song, Jiming Sa & Yijie Luo

  2. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China

    Jiming Sa

  3. College of Biomedical Engineering, South-Central Minzu University, Wuhan, 430074, China

    Zhushanying Zhang

  4. Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan, 430074, China

    Zhushanying Zhang

Authors
  1. Yuyan Song
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  2. Jiming Sa
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  3. Yijie Luo
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  4. Zhushanying Zhang
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Contributions

YS: Conceptualization, methodology, software, writing—original draft and editing. JS: Formal analysis and investigation, writing—review and editing, supervision. YL: Formal analysis and investigation, validation, visualization. ZZ: Formal analysis and investigation, supervision. All authors reviewed the manuscript.

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Correspondence to Jiming Sa.

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Song, Y., Sa, J., Luo, Y. et al. A comprehensively improved local binary pattern framework for texture classification. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19877-3

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