Local binary features for texture classification: Taxonomy and experimental study

作者：

Highlights：

• A taxonomy and comprehensive survey of LBP variants.

• Characteristics of, and connections between LBP variants are provided.

• A comprehensive experimental evaluation of 32 LBP methods.

• Comparison of 32 LBP variants with 8 deep ConvNets features.

• Evaluation of robustness to rotation, illumination, scale and noise changes.

• Comparison of computational complexity of forty variants.

摘要

Highlights•A taxonomy and comprehensive survey of LBP variants.•Characteristics of, and connections between LBP variants are provided.•A comprehensive experimental evaluation of 32 LBP methods.•Comparison of 32 LBP variants with 8 deep ConvNets features.•Evaluation of robustness to rotation, illumination, scale and noise changes.•Comparison of computational complexity of forty variants.

论文关键词：Texture classification,Local Binary Pattern,Rotation invariance,Noise robustness,Deep learning

论文评审过程：Received 16 August 2015, Revised 24 August 2016, Accepted 30 August 2016, Available online 6 September 2016, Version of Record 15 September 2016.

论文官网地址：https://doi.org/10.1016/j.patcog.2016.08.032