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A multi-scale threshold integration encoding strategy for texture classification

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Abstract

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks.

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Acknowledgments

The paper is funded by the National Key Research and Development Program of China (Grant No. 2016YFF0102806), the National Natural Science Foundation of China (Grant No. 51809056), the Natural Science Foundation of Heilongjiang Province, China (Grant No. F2017004).

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Authors and Affiliations

  1. College of Information and Communication Engineering, Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology Harbin Engineering University, Harbin, 150001, China

    Bin Li & Yibing Li

  2. Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada

    Q. M. Jonathan Wu

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  1. Bin Li
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  2. Yibing Li
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  3. Q. M. Jonathan Wu
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Correspondence to Yibing Li.

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Li, B., Li, Y. & Wu, Q.M.J. A multi-scale threshold integration encoding strategy for texture classification. Vis Comput 39, 5747–5761 (2023). https://doi.org/10.1007/s00371-022-02693-x

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