(feature similarity). Specifically, Lowe et al. [@lowe2004distinctive] proposed a nearest neighbor similarity ratio (NNSR) method that assigns a penalty equaling to the ratio of the closest to the second-closest feature distance to each correspondence and treats those correspondences with low ratios as inliers. Leordeanu et al. [@Leordeanu2005A] presented spectral technique (ST), where an affinity matrix is built using pairwise geometric constraints to remove mismatches in conflict with the most credible correspondences. Albarelli et al. [@albarelli2012imposing] casted the selection of correspondences in a game theoretic framework, known as game-theoretic matching (GTM), where a natural selection process allows corresponding points that satisfy a mutual distance constraint to thrive. Cho et al. [@Cho2010Reweighted] presented reweighted random walk algorithm (RRWM) for graph matching. An associated graph between two sets of candidate correspondences is drawn at first, and reliable nodes indicating the consistent correspondences in this graph are then selected by the reweighted random walk algorithm. Ma et al. [@Ma2017Locality] proposed locality preserving matching (LPM) to improve inlier selection by maintaining the local neighborhood structures of those potential true matches. Some non-parametric approaches that formulate the correspondence selection problem as a statistics problem have also been used, e.g., vector field consensus (VFC) [@Ma2014Robust] and grid-based 