Discriminant interest points are stable

A study of the performance of recently introduced discriminant methods for interest point detection [6, 14] is presented. It has been previously shown that the resulting interest points are more informative for object recognition than those produced by the detectors currently used in computer vision. Little is, however, known about the properties of discriminant points with respect to the metrics, such as repeatability, that have been traditionally used to evaluate interest point detection. A thorough experimental evaluation of the stability of discriminant points is presented, and this stability compared to those of four popular methods. In particular, we consider image correspondence under geometric and photometric transformations, and extend the experimental protocol proposed by Mikolajczyk et al. [13] for the evaluation of stability with respect to such transformations. The extended protocol is suitable for the evaluation of both bottom-up and top-down (learned) detectors. It is shown that the stability of discriminant interest points is comparable, and frequently superior, to those of interest points produced by various currently popular techniques.