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.

基于分类模拟仿射变换的区域特征描述符，通过将图像分类为不同的仿射程度来模拟仿射变换并生成新的图像集，进而根据这个新的图像集计算特征点的邻域信息，通过将特征点所属的最大稳定极值区域的灰度直方图和相对于特征点区域的灰度质心的规范化位置进行组合生成描述符，实验证明该描述符在仿射变换场景下具有比现有经典描述符更高的精度和鲁棒性，并且在与其他描述符集成时也显示出鲁棒性。