The local binary pattern (LBP) is an effective feature, describing the size relationship between the neighboring pixels and the current pixel. While individual LBP-based methods yield good results, co-occurrence LBP-based methods exhibit a better ability to extract structural information. However, most of the co-occurrence LBP-based methods excel mainly in dealing with rotated images, exhibiting limitations in preserving performance for scaled images. To address the issue, a cross-scale co-occurrence LBP (CS-CoLBP) is proposed. Initially, we construct an LBP co-occurrence space to capture robust structural features by simulating scale transformation. Subsequently, we use Cross-Scale Co-occurrence pairs (CS-Co pairs) to extract the structural features, keeping robust descriptions even in the presence of scaling. Finally, we refine these CS-Co pairs through Rotation Consistency Adjustment (RCA) to bolster their rotation invariance, thereby making the proposed CS-CoLBP as powerful as existing co-occurrence LBP-based methods for rotated image description. While keeping the desired geometric invariance, the proposed CS-CoLBP maintains a modest feature dimension. Empirical evaluations across several datasets demonstrate that CS-CoLBP outperforms the existing state-of-the-art LBP-based methods even in the presence of geometric transformations and image manipulations.

局部二进制模式 （LBP） 是一种有效的功能，用于描述相邻像素和当前像素之间的大小关系。虽然基于单个 LBP 的方法会产生良好的结果，但基于 LBP 的共现方法表现出更好的提取结构信息的能力。然而，大多数基于共现 LBP 的方法主要擅长处理旋转图像，在保持缩放图像的性能方面表现出局限性。为了解决这个问题，提出了一种跨尺度共生 LBP （CS-CoLBP）。最初，我们构建了一个 LBP 共现空间，通过模拟尺度变换来捕捉稳健的结构特征。随后，我们使用跨尺度共现对 （CS-Co 对） 来提取结构特征，即使在存在缩放的情况下也能保持稳健的描述。最后，我们通过旋转一致性调整 （RCA） 来改进这些 CS-Co 对，以增强它们的旋转不变性，从而使所提出的 CS-CoLBP 与现有的基于共现 LBP 的旋转图像描述方法一样强大。在保持所需的几何不变性的同时，所提出的 CS-CoLBP 保持了适度的特征维度。跨多个数据集的实证评估表明，即使在存在几何变换和图像处理的情况下，CS-CoLBP 也优于现有的最先进的基于 LBP 的方法。