Abstract
Image classification has attracted the attention in many research field. As an efficient and fast image feature extraction operator, LBP is widely used in the Image classification. The traditional local binary pattern (LBP) algorithm only considers the relationship between the center pixel and the edge pixel in the pixel region, which often leads to the problem of partial important information bias. To solve this problem, this paper proposes an improved LBP with threshold, which can significantly optimize the processing of texture features, and also be used to address the problems of multi-type image classification. The experimental results show that the algorithm can effectively improve the accuracy of image classification.
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Acknowledgement
This study is supported by National Natural Science Foundation of China (71901150, 71702111, 71971143), the Natural Science Foundation of Guangdong Province (2020A151501749), Shenzhen University Teaching Reform Project (Grants No. JG2020119).
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Xu, Z., Qiu, G., Li, W., He, X., Geng, S. (2021). Local Binary Pattern Algorithm with Weight Threshold for Image Classification. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_41
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DOI: https://doi.org/10.1007/978-3-030-78811-7_41
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