Training binary descriptors for improved robustness and efficiency in real-time matching

Abstract

Most descriptor-based keypoint recognition methods require computationally expensive patch preprocessing to obtain insensitivity to various kinds of deformations. This limits their applicability towards real-time applications on low-powered devices such as mobile phones. In this paper, we focus on descriptors which are relatively weak (i.e. sensitive to scale and rotation), and present a classification-based approach to improve their robustness and efficiency to achieve real-time matching. We demonstrate our method by applying it to BRIEF [7] resulting in comparable robustness to SIFT [4], while outperforming several state-of-the-art descriptors like SURF [6], ORB [8], and FREAK [10]. © 2013 Springer-Verlag.