  Correspondence selection aiming at seeking correct feature correspondences
from raw feature matches is pivotal for a number of feature-matching-based
tasks. Various 2D (image) correspondence selection algorithms have been
presented with decades of progress. Unfortunately, the lack of an in-depth
evaluation makes it difficult for developers to choose a proper algorithm given
a specific application. This paper fills this gap by evaluating eight 2D
correspondence selection algorithms ranging from classical methods to the most
recent ones on four standard datasets. The diversity of experimental datasets
brings various nuisances including zoom, rotation, blur, viewpoint change, JPEG
compression, light change, different rendering styles and multi-structures for
comprehensive test. To further create different distributions of initial
matches, a set of combinations of detector and descriptor is also taken into
consideration. We measure the quality of a correspondence selection algorithm
from four perspectives, i.e., precision, recall, F-measure and efficiency.
According to evaluation results, the current advantages and limitations of all
considered algorithms are aggregately summarized which could be treated as a
"user guide" for the following developers.
