Efficient Feature Matching via Nonnegative Orthogonal Relaxation

Abstract

Feature matching problem that incorporates pair-wise constraints can be formulated as an Integer Quadratic Programming (IQP) problem with one-to-one matching constraint. Since it is NP-hard, relaxation models are required. One main challenge for optimizing IQP matching is how to incorporate the discrete one-to-one matching constraint in IQP matching optimization. In this paper, we present a new feature matching relaxation model, called Nonnegative Orthogonal Relaxation (NOR), that aims to optimize IQP matching problem in nonnegative orthogonal domain. One important benefit of the proposed NOR model is that it can naturally incorporate the discrete one-to-one matching constraint in its optimization and can return a desired sparse (approximate discrete) solution for the problem. An efficient and effective update algorithm has been developed to solve the proposed NOR model. Promising experimental results on several benchmark datasets demonstrate the effectiveness and efficiency of the proposed NOR method.