Graph matching with nonnegative sparse model

Abstract

Graph matching is an essential problem in computer vision and pattern recognition. In this paper, we propose a novel graph matching method based on non-negative sparse model (NSGM). The main feature for our NSGM is that it can generate sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. In addition, an efficient algorithm was derived to solve NSGM problem. Promising experimental results on both synthetic and real image matching tasks show the effectiveness of the proposed matching method. © 2013 Springer-Verlag.