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A neighbourhood feature-based local binary pattern for texture classification

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Abstract

The CNN framework has gained widespread attention in texture feature analysis; however, handcrafted features still remain advantageous if computational cost needs to take precedence and in cases where textures are easily extracted with few intra-class variation. Among the handcrafted features, the local binary pattern (LBP) is extensively applied for analysing texture due to its robustness and low computational complexity. However, in local difference vector, it only utilizes the sign component, resulting in unsatisfactory classification capability. To improve classification performance, most LBP variants employ multi-feature fusion. Nevertheless, this can lead to redundant and low-discriminative sub-features and high computational complexity. To address these issues, we propose the neighbourhood feature-based local binary pattern (NF-LBP). Inspired by gradient’s definition, we extract the neighbourhood feature in a local region by simply using the first-order difference and 2-norm. Next, we introduce the neighbourhood feature (NF) pattern to describe intensity changes in the neighbourhood. Finally, we combine the NF pattern with the local sign component and the centre pixel component to create the NF-LBP descriptor. This approach provides better complementary texture information to traditional local sign pattern and is less sensitive to noise. Additionally, we use an adaptive local threshold in the encoding scheme. Our experimental results of classification accuracy and F1 score on five texture databases demonstrate that our proposed NF-LBP method attains outstanding texture classification performance, outperforming existing state-of-the-art approaches. Furthermore, extensive experimental results reveal that NF-LBP is strongly robust to Gaussian noise and salt-and-pepper noise.

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Data Availability

The datasets that underpin the findings of this study are openly available at the following URLs: http://www.outex.oulu.fi, www.cs.columbia.edu/CAVE/curet, and www.computer.org/publications/dlib. The program that was used to generate the findings of this study is available upon request from the corresponding author. Any questions related to the datasets or the program may also be directed to the corresponding author.

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Funding

This work is supported in part by the National Natural Science Foundation of China (Grant No. 62275211, 61675161, U1903213) and the Open Research Fund of State Key Laboratory of Transient Optics and Photonics (Grant No. SKLST202212).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Shaokun Lan, Jie Li & Zhibin Pan

  2. AVIC Xi’an Flight Automatic Control Research Institute, Xi’an, 710076, China

    Shiqi Hu

  3. Institute of Information and Navigation, Air Force Engineering University, Xi’an, 710077, China

    Hongcheng Fan

  4. State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences, Xi’an, 710119, China

    Zhibin Pan

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  1. Shaokun Lan
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Correspondence to Zhibin Pan.

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Lan, S., Li, J., Hu, S. et al. A neighbourhood feature-based local binary pattern for texture classification. Vis Comput 40, 3385–3409 (2024). https://doi.org/10.1007/s00371-023-03041-3

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