Neighborhood matching for object recognition algorithms based on local image features

Abstract

Local image features around interest-points have been widely used in order to exploit the similarities between different views of an object in different images. While there are numerous algorithms on detecting the interest-points and defining the local features, few have focused on the importance of the matching process. In this paper, we presented a method that matches interest-points detected via any algorithm. The method is motivated from human perceptual rules, particularly the Gestalt Psychology, and realizes the fact that “The whole is different from the sum of its parts”. The efficacy of the algorithm is not only the ability to decrease the number of false positive matches but also to increase the number of true positives, yielding rock-steady results for any algorithm based on matching local features.