LPM [@Ma2017Locality] are selected as they all rely on the geometric consensus. VFC [@Ma2014Robust] and the recent GMS [@bian2017gms] are taken into consideration since they eliminate outliers from the perspective of statistical measures. In order to compare those methods from different perspectives, we choose four standard datasets, i.e., VGG [@Mikolajczyk2005A], Heinly [@Heinly2012Comparative], Symbench [@Snavely2012Image], AdelaideRMF [@Wong2011Dynamic], as experimental platforms under the motivation to test those correspondence selection methods’ overall performance when faced with a variety of nuisances rather than in their favoring circumstances. For instance, geometric constraints may turn to be vulnerable under rigid/non-rigid transformations such as zoom and rotation; feature similarity constraints are suspicious when the image undergoes blur and light changes; parametric models (the homography matrix) can hardly cope with scenes with parallax (all above conclusions have been verified in Sect. \[sec:exper\]). The considered datasets well cover these concerns. To be specific, VGG is a hybrid dataset containing challenges including zoom, rotation, blur, JPEG compression, light and viewpoint change. Heinly contains pure zoom and rotation. Symbench involves scenes with light changes and varying rendering styles. AdelaideRMF possesses viewpoint change and multi-structures, resulting in parallax. The behavior of each method is quantitatively measured using precision, recall and F-measure [@bian2017gms; 