Selecting correct matches from a set of tentative feature point correspondences plays a vital important role in many tasks, such as structure from motion (SfM), wide baseline stereo and image search. In this paper, we propose an efficient and effective method for identifying correct matches from an initial batch of feature correspondences. The proposed method first obtains a subset of correct matches based on the assumption that the local geometric structure among a feature point and its nearest neighbors in an image cannot be easily affected by both geometric and photometric transformations, and thus should be observed in the matched images. For efficiency, we model this local geometric structure by a set of linear coefficients that reconstruct the point from its neighbors. After obtaining a portion of correct matches, we then provide two ways to accurately estimate the correctness of each match and to efficiently estimate the number of correct matches, respectively. The proposed method is evaluated on both applications including image matching and image re-ranking. Experimental results on several public datasets show that our method outperforms state-of-the-art techniques in terms of speed and accuracy. 
Exploiting local linear geometric structure for identifying correct matches