Completed robust local binary pattern for texture classification

Authors:
Yang Zhao;Wei Jia;Rong-Xiang Hu;Hai Min
Affiliations:
Department of Automation, University of Science and Technology of China, Hefei 230027, China and Institute of Intelligent Machines, Chinese Academy of Science, Hefei 230031, China;Institute of Nuclear Energy Safety Technology, Chinese Academy of Science, Hefei 230031, China;Institute of Nuclear Energy Safety Technology, Chinese Academy of Science, Hefei 230031, China;Department of Automation, University of Science and Technology of China, Hefei 230027, China and Institute of Intelligent Machines, Chinese Academy of Science, Hefei 230031, China
Venue:
Neurocomputing
Year:
2013

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Abstract

Original Local Binary Pattern (LBP) descriptor has two obvious demerits, i.e., it is sensitive to noise, and sometimes it tends to characterize different structural patterns with the same binary code which will reduce its discriminability inevitably. In order to overcome these two demerits, this paper proposes a robust framework of LBP, named Completed Robust Local Binary Pattern (CRLBP), in which the value of each center pixel in a 3x3 local area is replaced by its average local gray level. Compared to the center gray value, average local gray level is more robust to noise and illumination variants. To make CRLBP more robust and stable, Weighted Local Gray Level (WLG) is introduced to take place of the traditional gray value of the center pixel. The experimental results obtained from four representative texture databases show that the proposed method is robust to noise and can achieve impressive classification accuracy.