Scale-selective and noise-robust extended local binary pattern for texture classification

作者：

Highlights：

• A novel texture descriptor to address both scale transformation and noise interference.

• An extended LBP with lightweight feature dimension.

• Maintains both macro and micro descriptive information in the spatial and spectral domains.

• Outperforms thirty classical LPB variants as well as eight typical deep learning methods.

• Experiments on five public databases and one fresh texture database.

摘要

•A novel texture descriptor to address both scale transformation and noise interference.•An extended LBP with lightweight feature dimension.•Maintains both macro and micro descriptive information in the spatial and spectral domains.•Outperforms thirty classical LPB variants as well as eight typical deep learning methods.•Experiments on five public databases and one fresh texture database.

论文关键词：Local binary pattern (LBP),Texture descriptor,Feature extraction,Texture classification

论文评审过程：Received 27 November 2021, Revised 12 June 2022, Accepted 13 July 2022, Available online 27 July 2022, Version of Record 2 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108901