Supervised learning for parameterized Koopmans–Beckmann’s graph matching - Zeng, Shaofeng; Liu, Zhiyong; Yang, Xu (Pattern recognition letters, 2021)
•Discusses a novel graph matching model, i.e., parameterized Koopmans–Beckmann’s graph matching (KBGMw).•Proposes a supervised learning method for KBGMw.•Shows the performances of the proposed method and several state-of-the-art graph matching methods.•Summarizes the advantages and disadvantages of the proposed method. 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.