Local binary pattern (LBP) is sensitive to inverse grayscale changes. To overcome this problem, several methods map each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited description ability. In this paper, we introduce a generic histogram sorting method which exploits pattern transition rules to preserve the distribution information of LBP codes and their complements. Based on this method, we develop a series of sorted LBP (SLBP) descriptors, including pairwise sorted ones and fully sorted ones, which are all invariant to grayscale inversion and image rotation. Since SLBP focuses on encoding difference-sign information, it is further generalized to embed difference-magnitude LBP features to obtain complementary representations. We also propose an invariant pyramid pooling strategy to aggregate SLBP features into a pyramid image representation. Experiments on several benchmark texture databases and one newly collected image database (grayscale-inversion images, GII) demonstrate the effectiveness of our descriptors for image classification under (linear or nonlinear) grayscale-inversion and rotation changes. The source code will be available at https://github.com/stc-cqupt/slbp.

局部二进制模式 (LBP) 对逆灰度变化很敏感。为了克服这个问题，有几种方法将每个 LBP 代码及其补充映射到最小的一个。然而，在不区分 LBP 代码及其补充的情况下，这些方法表现出有限的描述能力。在本文中，我们介绍了一种通用直方图排序方法，该方法利用模式转换规则来保留 LBP 代码及其补充的分布信息。基于这种方法，我们开发了一系列排序LBP（SLBP）描述符，包括成对排序的和完全排序的，它们都对灰度反转和图像旋转保持不变。由于 SLBP 专注于编码差异符号信息，因此进一步推广到嵌入差异幅度 LBP 特征以获得互补表示。我们还提出了一种不变的金字塔池化策略，将 SLBP 特征聚合为金字塔图像表示。在几个基准纹理数据库和一个新收集的图像数据库（灰度反转图像，GII）上的实验证明了我们的描述符在（线性或非线性）灰度反转和旋转变化下的图像分类的有效性。源代码将在 https://github.com/stc-cqupt/slbp 上提供。