  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.
