Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns. The feature mapping models of gLBP descriptors are constructed by the maximal relative-variation rate (mr2) of rotation-invariant patterns, and then prestored as mapping lookup files. By mapping, initial patterns can be transformed into low-dimensional ones. And then it generates multiresolution texture features via the joint and concatenation of gLBP descriptors on different sampling parameters. A trained nearest neighbor classifier with chi-square distance is applied to classify textures by feature histograms. The experimental results of simulation on five public texture databases show that the proposed method is reliable and efficient in texture classification. In comparison with nine other similar approaches, including two state-of-the-art ones, the proposed method runs faster than most of them and also outperforms all of them in terms of classification accuracy and noise robustness. It achieves higher accuracy and has also better robustness to the Salt&Pepper and Gaussian noise added artificially into texture images.

中文翻译：

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用于多分辨率纹理分类的基于训练的梯度 LBP 特征模型


局部二进制模式（LBP）是一种简单而高效的用于提取纹理特征的编码模型。为了改进纹理分类，本文设计了中值采样规则，定义了一组梯度LBP（gLBP）描述符，提出了一种基于训练的特征模型映射方法，然后利用四个gLBP描述符的多分辨率特征融合开发了纹理分类框架。四个描述符通过中值采样配合，分别通过中心梯度、径向梯度、幅度梯度和切线梯度对像素进行编码，生成初始gLBP模式。 gLBP描述符的特征映射模型是通过旋转不变模式的最大相对变化率（mr2）构建的，然后预存储为映射查找文件。通过映射，初始模式可以转换为低维模式。然后通过不同采样参数上的 gLBP 描述符的联合和级联生成多分辨率纹理特征。应用经过训练的具有卡方距离的最近邻分类器通过特征直方图对纹理进行分类。在五个公共纹理数据库上的仿真实验结果表明，该方法在纹理分类方面可靠、高效。与其他九种类似方法（包括两种最先进的方法）相比，所提出的方法比大多数方法运行得更快，并且在分类精度和噪声鲁棒性方面也优于所有方法。它实现了更高的精度，并且对人工添加到纹理图像中的椒盐噪声和高斯噪声也具有更好的鲁棒性。