A Completed Modeling of Local Binary Pattern Operator for Texture Classification
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
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DOI：
10.1109/tip.2010.2044957
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发表时间：
2010-06-01
影响因子：
10.6
通讯作者：
Zhang, David
中科院分区：
文献类型：
--
作者：
Guo, Zhenhua;Zhang, Lei;Zhang, David
在此通信中，提出了局部二值模式（LBP）算子的完整建模，并开发了相应的完整LBP （CLBP）纹理分类方案。局部区域由中心像素和局部差值-幅度变换（LDSMT）表示。中心像素表示图像灰度值，通过全局阈值法将其转换为二进位码，即CLBP-Center （CLBP_C）。LDSMT将图像局部差异分解为符号和幅度两个互补分量，并提出CLBP-Sign （CLBP_S）和CLBP-Magnitude （CLBP_M）两个算子对其进行编码。传统的LBP相当于CLBP的CLBP_S部分，我们发现CLBP_S比CLBP_M保留了更多的局部结构信息，这解释了为什么简单的LBP算子可以很好地提取纹理特征。将CLBP_S、CLBP_M和CLBP_C特征组合成联合分布或混合分布，可以显著改善旋转不变纹理分类。
In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT). The center pixels represent the image gray level and they are converted into a binary code, namely CLBP-Center (CLBP_C), by global thresholding. LDSMT decomposes the image local differences into two complementary components: the signs and the magnitudes, and two operators, namely CLBP-Sign (CLBP_S) and CLBP-Magnitude (CLBP_M), are proposed to code them. The traditional LBP is equivalent to the CLBP_S part of CLBP, and we show that CLBP_S preserves more information of the local structure than CLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well. By combining CLBP_S, CLBP_M, and CLBP_C features into joint or hybrid distributions, significant improvement can be made for rotation invariant texture classification.