Local binary features for texture classification: Taxonomy and experimental study
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- 作者：Li Liua ; liuli_nudt@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Paul Fieguthb ; pfieguth@uwaterloo.ca" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yulan Guoc ; yulan.guo@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Xiaogang Wangd ; xgwang@ee.cuhk.edu.hk" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Matti Pietikä ; inene ; matti.pietikainen@ee.oulu.fi" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Texture classification ; Local Binary Pattern ; Rotation invariance ; Noise robustness ; Deep learning
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：62
- 期：Complete
- 页码：135-160
- 全文大小：2244 K
文摘
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A taxonomy and comprehensive survey of LBP variants.
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Characteristics of, and connections between LBP variants are provided.
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A comprehensive experimental evaluation of 32 LBP methods.
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Comparison of 32 LBP variants with 8 deep ConvNets features.
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Evaluation of robustness to rotation, illumination, scale and noise changes.
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Comparison of computational complexity of forty variants.