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2D feature correspondence, feature matching, correspondence selection, inliers

Introduction {#sec:intr}
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Feature correspondence selection is a fundamental and critical task in computer vision and robotics. It is the basis for a wide range of applications, such as structure-from-motion [@snavely2008modeling], simultaneous localization and mapping [@benhimane2004real], tracking [@hare2012efficient], image stitching [@brown2007automatic], and object recognition [@lowe1999object], to name just a few.

The main purpose of correspondence selection is retrieving as many as correct correspondences (also known as *inliers*) from the initial feature matches. Usually, this task is under the background of feature matching. The general process of feature matching starts by detecting representative points, namely keypoints, for two images to be matched. Then, local descriptors such as SIFT [@lowe2004distinctive] and ORB [@rublee2011orb] are employed to perform feature description for those keypoints. To build the connection between two images, keypoints with similar feature descriptors are matched, generating a set of raw feature matches. However, the initial feature matches often suffer from severe wrong matches (as shown in Fig. \[fig:inspr\](a)) due to the limited distinctiveness of feature descriptors or/and external interferences such as noise and occlusion. This problem makes correspondence selection a necessity for accurate feature matching. 