Image Feature Matching via Progressive Vector Field Consensus

In this letter, we propose a simple yet effective approach, named Progressive Vector Field Consensus (PVFC), for addressing the problem of finding more true feature correspondences between images. The key idea is to progressively perform feature matching based on Vector Field Consensus, and hence greatly boost the number of true matches as well as avoid false matches. More specifically, it uses matching results on a small putative correspondence set with high inlier ratio to guide the matching on a large putative correspondence set which probably covers the whole true correspondences. We model the transformation between images in a reproducing kernel Hilbert space, and a sparse approximation is applied to the transformation to avoid high computational complexity. Our results quantitatively show that our PVFC outperforms state-of-the-art methods, both in accuracy and in efficiency. Moreover, the progressive framework is general and can be applied to other cases for robust estimation.