GlcMatch: global and local constraints for reliable feature matching

A match is considered as an incorrect match when the matched features in two views do not correspond to the same physical location. It is inevitable that generates mismatches at a local descriptor level. Differentiating true and false matches remains a challenge, especially in the case of ambiguities, wide baselines, and strong illumination variations, which might contain a large number of mismatches (even up to 90%). In this paper, we develop GlcMatch, an outlier rejection method that takes advantage of both global and local constraints to classify putative matches. Specifically, we use vector field consistency to form continuous global smoothness and use triangular mesh constraints to implement the local piecewise smoothness. Evaluation on benchmark datasets demonstrates GlcMatch can obtain large numbers of good quality correspondences and achieve significant performance.