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Graph Matching Based on Fast Normalized Cut

  • Jing Yang
  • Xu Yang
  • Zhang-Bing Zhou
  • Zhi-Yong Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

Graph matching is important in pattern recognition and computer vision which can solve the point correspondence problems. Graph matching is an NP-hard problem and approximate relaxation methods are used to solve this problem. But most of the existing relaxation methods solve graph matching problem in the continues domain without considering the discrete constraints. In this paper, we propose a fast normalized cut based graph matching method which takes the discrete constraints into consideration. Specifically, a regularization term which is related to the discrete form of the permutation matrix is added to the objective function. Then, the objective function is transformed to a form which is similar to the fast normalized cut framework. The fast normalized cut algorithm is generalized to get the permutation matrix iteratively. The comparisons with the state-of-the-art methods validate the effectiveness of the proposed method by the experiments on synthetic data and image sequences.

Keywords

Graph matching Fast normalized cut Discrete constraints 

Notes

Acknowledgments

This work is supported partly by the National Natural Science Foundation (NSFC) of China (grants 61772479, 61662021, 61773047, 61503383, 61633009, U1613213, 61627808, 61502494, and U1713201), partly by the National Key Research and Development Plan of China (grant 2016YFC0300801 and 2017YFB1300202), and partly by the Development of Science and Technology of Guangdong Province Special Fund project (grant 2016B090910001).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jing Yang
    • 1
    • 2
  • Xu Yang
    • 2
  • Zhang-Bing Zhou
    • 1
    • 3
  • Zhi-Yong Liu
    • 2
    • 4
    • 5
  1. 1.School of Information EngineeringChina University of Geosciences (Beijing)BeijingChina
  2. 2.State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Computer Science DepartmentTELECOM SudParisEvryFrance
  4. 4.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
  5. 5.University of Chinese Academy of SciencesBeijingChina

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