Noise tolerant local binary pattern operator for efficient texture analysis

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• 作者：Abdolhossein Fathi ; ^{fathi@eng.ui.ac.ir} ; Ahmad Reza Naghsh-Nilchi¹ ; ^{nilchi@eng.ui.ac.ir}
• 关键词：Local binary pattern ; Texture pattern descriptor ; Texture analysis
• 刊名：Pattern Recognition Letters
• 出版年：2012
• 出版时间：1 July, 2012
• 年：2012
• 卷：33
• 期：9
• 页码：1093-1100
• 全文大小：1045 K

文摘

The local binary pattern (LBP) operator is a very effective multi-resolution texture descriptor that can be applied in many image processing applications. However, existing LBP operators can not use the information of non-uniform patterns efficiently and they are also sensitive to noise. This paper proposes a noise tolerant extension of LBP operators to extract statistical and structural image features for efficient texture analysis. The proposed LBP operator uses a circular majority voting filter and suitable rotation-invariant labeling scheme to obtain more regular uniform and non-uniform patterns that have better discrimination ability and more robustness against noise. Experimental results on the Brodatz, CUReT and MeasTex databases show the improvement of the proposed LBP operator performance, especially when a large number of neighbors are used for extracting texture patterns.