To address the issue of feature descriptors being ineffective in representing grayscale feature information when images undergo high affine transformations, leading to a rapid decline in feature matching accuracy, this paper proposes a region feature descriptor based on simulating affine transformations using classification. The proposed method initially categorizes images with different affine degrees to simulate affine transformations and generate a new set of images. Subsequently, it calculates neighborhood information for feature points on this new image set. Finally, the descriptor is generated by combining the grayscale histogram of the maximum stable extremal region to which the feature point belongs and the normalized position relative to the grayscale centroid of the feature point's region. Experimental results, comparing feature matching metrics under affine transformation scenarios, demonstrate that the proposed descriptor exhibits higher precision and robustness compared to existing classical descriptors. Additionally, it shows robustness when integrated with other descriptors.

翻译：为解决图像经历高仿射变换时，特征描述符在表征灰度特征信息方面失效，导致特征匹配精度急剧下降的问题，本文提出一种基于分类模拟仿射变换的区域特征描述子。首先，本方法对不同仿射程度的图像进行分类，以模拟仿射变换并生成一组新的图像；随后，在该新图像集上计算特征点的邻域信息；最后，结合特征点所属最大稳定极值区域的灰度直方图以及特征点区域灰度质心的归一化位置，生成描述子。在仿射变换场景下进行的特征匹配指标对比实验表明，与现有经典描述子相比，本文提出的描述子具有更高的精度和鲁棒性；同时，与其他描述子联合使用时亦表现出鲁棒性。