A contrario matching of SIFT-like descriptors

In this paper, the matching of SIFT-like features [5] between images is studied. The goal is to decide which matches between descriptors of two datasets should be selected. This matching procedure is often a preliminary step towards some computer vision applications, such as object detection and image registration for instance. The distances between the query descriptors and the database candidates being computed, the classical approach is to select for each query its nearest neighbor, depending on a global threshold on dissimilarity measure. In this contribution, an a contrario framework for the matching procedure is introduced, based on a threshold on a probability of false detections. This approach yields dissimilarity thresholds automatically adapted to each query descriptor and to the diversity and size of the database. We show on various experiments on a large image database, the ability of such a method to decide whether a query and its candidates should be matched.