Keypoint clustering for robust image matching
Sundeep Vaddadi, Onur C. Hamsici, Yuriy A. Reznik, John Hong, Chong Lee · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
A number of popular image matching algorithms such as Scale Invariant Feature Transform (SIFT)1 are based on local image features. They first detect interest points (or keypoints) across an image and then compute descriptors based on patches around them. In this paper, we observe that in textured or feature-rich images, keypoints typically appear in clusters following patterns in the underlying structure. We show that such clustering phenomenon can be used to: 1) enhance recall and precision performance of the descriptor matching process, and 2) improve convergence rate of the RANSAC algorithm used in the geometric verification stage.