Joint-scale LBP: a new feature descriptor for texture classification
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This paper presents a simple, efficient, yet robust approach, named joint-scale local binary pattern (JLBP), for texture classification. In the proposed approach, the joint-scale strategy is developed firstly, and the neighborhoods of different scales are fused together by a simple arithmetic operation. And then, the descriptor is extracted from the mutual integration of the local patches based on the conventional local binary pattern (LBP). The proposed scheme can not only describe the micro-textures of a local structure, but also the macro-textures of a larger area because of the joint of multiple scales. Further, motivated by the completed local binary pattern (CLBP) scheme, the completed JLBP (CJLBP) is presented to enhance its power. The proposed descriptor is evaluated in relation to other recent LBP-based patterns and non-LBP methods on popular benchmark texture databases, Outex, CURet and UIUC. Generally, the experimental results show that the new method performs better than the state-of-the-art techniques.
KeywordsTexture classification Local binary pattern (LBP) Joint-scale local binary pattern (JLBP) Complete JLBP (CJLBP)
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that we refer to in this paper. We would also like to thank Dr. Guo and Dr. Zhao as well as the MVG group for sharing their codes. This work is sponsored by the NSFC (No. 61572173) and the basic and advanced technology research project of Henan Province (Nos. 132300410462, 112300410281), the research team of HPU (No. T2014-3).
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