Supervised learning for parameterized Koopmans–Beckmann’s graph matching

Abstract In this paper, we discuss a novel graph matching problem, namely the parameterized Koopmans–Beckmann’s graph matching (KBGMw). KBGMw is defined by a weighted linear combination of a series of Koopmans–Beckmann’s graph matching. First, we show that KBGMw can be taken as a special case of the parameterized Lawler’s graph matching, subject to certain conditions. Second, based on structured SVM, we propose a supervised learning method for automatically estimating the parameters of KBGMw. Experimental results on both synthetic and real image matching data sets show that the proposed method achieves relatively better performances, even superior to some deep learning methods.