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Texture classification combining improved local binary pattern and threshold segmentation
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DOI：10.1007/s11042-023-14749-8.png)
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Abstract
En 中文
Local Binary Pattern (LBP) is a feature extraction operator with both high texture discrimination ability and low computational complexity. Many LBP variants have been proposed to improve the performance of texture classification or overcome the drawbacks of LBP. There are three shortcomings in some LBP variants: discarding the magnitude component between local differences, adopting fixed weights in the encoding process and discarding the absolute information of the pixel gray level. Based on the three points, this paper proposes an improved LBP with two operators, local binary pattern operator based on magnitude ranking and global threshold segmentation operator, to further improve the performance. This improved LBP can achieve excellent texture classification accuracy across six common datasets, with an average of 1% lower than the best LBP variants. Meanwhile, the computational complexity of the proposed improved LBP is several times lower than that of the best LBP variants.
Keywords:
Texture classification
Local binary pattern
Feature extraction
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