Median Robust Extended Local Binary Pattern for Texture Classification
Median Robust Extended Local Binary Pattern for Texture Classification
复制标题
用于纹理分类的中值鲁棒扩展局部二进制模式
DOI：
10.1109/tip.2016.2522378
复制
发表时间：
2016-03-01
影响因子：
10.6
通讯作者：
Pietikainen, Matti
中科院分区：
文献类型：
--
作者：
Liu, Li;Lao, Songyang;Pietikainen, Matti
局部二进制模式（LBP）被认为是计算效率最高的高性能纹理特征之一。然而，LBP方法对图像噪声非常敏感，并且无法捕获宏观结构信息。为了最好地解决这些缺点，在本文中，我们介绍了一种新的描述符纹理分类，中值鲁棒扩展LBP（MSPBP）。与传统的LBP和许多LBP变体不同，MPEGBP比较区域图像中值而不是原始图像强度。多尺度LBP类型描述符计算有效地比较图像中值在一种新的采样方案，它可以捕获微观和宏观纹理信息。对基准数据集的综合评价表明，MCPBP算法对灰度变化、旋转变化和噪声具有很强的鲁棒性，但计算成本很低。在三个流行的Outex测试套件上，MPEGBP产生了99.82%、99.38%和99.77%的最佳分类分数。更重要的是，MPEGBP被证明是高度鲁棒的图像噪声，包括高斯噪声，高斯模糊，盐和胡椒噪声，和随机像素损坏。
Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.