Robust Image Feature Point Matching Based on Structural Distance

Feature point matching is a key step of image registration, object recognition and many other computer vision applications. By using the proposed structural distance between feature point sets as the matching similarity, we are able to match the spatial structures of feature points in different images. In the optimization process of the structural distance, both local and global relationship are considered, which greatly improves the robustness and accuracy. We also present a fast algorithm with higher efficiency, which approximately realizes this method by linear matrix multiplication operations. The proposed method achieve promising matching results in the experiments.