Fig. \[fig:inspr\](b) shows that those matches after correspondence selection are far more consistent than the initial feature matches. This consensus allows massive high-level vision tasks. For instance, homography, affine and essential matrices can be estimated from those consistent correspondences, thus allowing us to compute the transformation between two images and warp them into a unified coordinate system [@brown2007automatic]. Other applications also involve camera parameter estimation [@snavely2008modeling] and object tracking [@hare2012efficient]. Nonetheless, the correspondence selection problem is difficult in real applications due to several factors, e.g., zoom, rotation, blur, viewpoint change, JPEG compression, light change, different rendering styles, multi-structures, and etc. Different scenarios will also lead to different distributions of feature matches which are linearly non-separable.

To address these problems, many approaches that have been presented during the past two decades can be divided into two categories [@Ma2014Robust]: parametric and non-parametric methods. (i) For parametric methods, they seek consistent correspondences grounded on parametric geometric models. Typical methods include the random sample consensus (RANSAC) [@fischler1981random], the progressive sample consensus (PROSAC) [@Chum2005Matching], the universal framework for random sample consensus (USAC) [@Raguram2013USAC], and etc. (ii) For non-parametric methods, they are independent from parametric model assumptions. Some of them search correspondence inliers via either feature 