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A Novel Method for Graph Matching

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Published:23 October 2020Publication History

ABSTRACT

Graph matching is a fundamental NP-hard problem in computer science. We propose an approximate graph matching method. To match the nodes of two graphs, our method first constructs an association graph. For each pair of nodes within the association graph, our method computes their mutual consistency on the basis of the distance information and angle information of the original graphs' nodes. The consistencies of all pairs of nodes form an affinity matrix. With the affinity matrix, our method then performs random walks on the association graph to achieve a stable quasi-stationary distribution. Discretizing the distribution on the basis of the Hungarian algorithm, our method finally obtains the matching between the nodes of the two original graphs. The experimental results demonstrate the effectiveness of our method on graph matching.

References

  1. Riesen, K., Jiang, X., and Bunke, H. 2010. Exact and inexact graph matching: methodology and applications, 217--247. DOI= https://link.springer.com/chapter/10.1007%2F978-1-4419-6045-0_7.Google ScholarGoogle Scholar
  2. Gold, S., Rangarajan, A. 1996. A graduated assignment algorithm for graph matching, 377--388. DOI= https://ieeexplore.ieee.org/document/491619.Google ScholarGoogle Scholar
  3. Cho, M., Lee, J., and Lee, K. M. 2010. Reweighted random walks for graph matching, 492--505. DOI= https://link.springer.com/chapter/10.1007%2F978-3-642-15555-0_36.Google ScholarGoogle Scholar
  4. Mills-Tettey, G. A., Stentz, A., Dias, M. B. 2007. The dynamic Hungarian algorithm for the assignment problem with changing costs. Carnegie Mellon University. DOI= http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.69.8170.Google ScholarGoogle Scholar
  5. Leordeanu, M., Hebert, M. 2005. A spectral technique for correspondence problems using pair wise constraints, 14821489. DOI= https://ieeexplore.ieee.org/document/1544893?arnumber=1544893.Google ScholarGoogle Scholar
  6. Timothée Cour, Srinivasan, P., Shi, J. 2006. Balanced Graph Matching. Conference on Advances in Neural Information Processing Systems, 313--320. DOI= https://ieeexplore.ieee.org/document/6287355/.Google ScholarGoogle Scholar
  7. Jiang, B., Zhao, H., Tang, J., and Luo, B. 2014. A sparse nonnegative matrix factorization technique for graph matching problems. Pattern Recognition.47(2), 736--747. DOI=https://www.sciencedirect.com/science/article/abs/pii/S0031320313003476.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Jiang B, Tang J, Ding CH, Luo B. 2017. Nonnegative orthogonal graph matching. In: Association for the advance of artificial intelligence, 4089--4095. DOI= https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14405.Google ScholarGoogle Scholar
  9. Cour T, Shi J. 2007. Solving Markov random fields with spectral relaxation. In: Artificial intelligence and statistics, 75--82. DOI= https://www.researchgate.net/publication/220319870_Solving_Markov_Random_Fields_with_Spectral_Relaxation.Google ScholarGoogle Scholar
  10. Nie W, Ding H, Liu A, Deng Z, Su Y. 2018. Subgraph learning for graph matching. Pattern Recognition Letters. DOI=https://www.sciencedirect.com/science/article/abs/pii/S0167865518302897.Google ScholarGoogle Scholar
  11. Wang T, Ling H, Lang C, Feng S. 2017. Graph Matching with Adaptive and Branching Path Following. In: IEEE transactions on pattern analysis and machine intelligence, 2853--2867. DOI= https://ieeexplore.ieee.org/document/8089364.Google ScholarGoogle Scholar
  12. D. Khue Le-Huu, Paragios, N. 2017. Alternating Direction Graph Matching. In: IEEE Conference on computer vision and pattern recognition, 6253--6261. DOI= https://ieeexplore.ieee.org/document/8100005.Google ScholarGoogle ScholarCross RefCross Ref
  13. Leordeanu, M., Hebert, M., and Sukthankar, R. 2009. An Integer Projected Fixed Point Method for Graph Matching and MAP Inference. In: Conference on Neural Information Processing Systems, 1114--1122. DOI= https://www.researchgate.net/publication/221619668_An_Int eger_Projected_Fixed_Point_Method_for_Graph_Matching_and_MAP_Inferencehttps://www.researchgate.net/publication/221619668_An_Integer_Projected_Fixed_Point_Method_for_Graph_Matching_and_MAP_Inference.Google ScholarGoogle Scholar
  14. D. Khuê Lê-Huu, Paragios, N. 2017. Alternating direction graph matching. In: IEEE Conference on computer vision and pattern recognition, 6253--6261. DOI= https://ieeexplore.ieee.org/document/8100005.Google ScholarGoogle ScholarCross RefCross Ref
  15. Cho, M., Sun, J., Duchenne, O., Ponce, J. 2014. Finding matches in a haystack: a max-pooling strategy for graph matching in the presence of outliers. In: IEEE Conference on computer vision and pattern recognition, 2083--2090. DOI= https://ieeexplore.ieee.org/document/8089364.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Jiang, B., Tang, J., Luo, B. 2019. Efficient Feature Matching via Nonnegative Orthogonal Relaxation. International Journal of Computer Vision, 127(9), 1345--1360. DOI= https://link.springer.com/article/10.1007/s11263-019-01185-1.Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Other conferences
      ICBDT '20: Proceedings of the 3rd International Conference on Big Data Technologies
      September 2020
      250 pages
      ISBN:9781450387859
      DOI:10.1145/3422713

      Copyright © 2020 ACM

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      Publication History

      • Published: 23 October 2020

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