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International Workshop on Graph-Based Representations in Pattern Recognition

GbRPR 2011: Graph-Based Representations in Pattern Recognition pp 175–184Cite as

Graph Matching Based on Dot Product Representation of Graphs

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6658))

Abstract

This paper proposes an efficient algorithm for inexact graph matching. Our main contribution is that we render the graph matching process to a way of recovery missing data based on dot product representation of graph (DPRG). We commence by building an association graph using the nodes in graphs with high matching probabilities, and treat the correspondences between unmatched nodes as missing data in association graph. Then, we recover correspondence matches using dot product representation of graphs with missing data. Promising experimental results on both synthetic and real-world data show the effectiveness of our graph matching method.

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References

  1. Sanfeliu, A., Fu, K.S.: A Distance Measure between Attributed Relational Graphs for Pattern Recognition. IEEE Trans. Syst. Man Cybernetics. 13(3), 353–362 (1983)

    Article  MATH  Google Scholar 

  2. Shapiro, L.G., Haralick, R.M.: A Metric for Comparing Relational Descriptions. IEEE PAMI 7(1), 90–94 (1985)

    Article  Google Scholar 

  3. Tsai, W.H., Fu, K.S.: Error-correcting isomorphisms of attributed relational graphs for pattern analysis. IEEE Trans. Syst. Man Cybernetics. 9, 757–768 (1979)

    Article  MATH  Google Scholar 

  4. Eshera, M.A., Fu, K.S.: A similarity measure between attributed relational graphs for image analysis. In: Proc. 7th Int. Conf. Pattern Recognition, pp. 75–77 (1984)

    Google Scholar 

  5. Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: An efficient algorithm for the inexact matching of ARG graphs using a contextual transformational model. In: Proc.13th Int. Conf. Pattern Recognition, pp. 180–184 (1996)

    Google Scholar 

  6. Llados, J., Marti, E., Villanueva, J.J.: Symbol recognition by error-tolerant sub-graph matching between region adjacency graphs. IEEE PAMI 23, 1137–1143 (2001)

    Article  Google Scholar 

  7. Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. IEEE PAMI 18 (1996)

    Google Scholar 

  8. Christmas, W.J., Kittler, J., Petrou, M.: Structural matching in computer vision using probabilistic relaxation. IEEE PAMI 17, 749–764 (1995)

    Article  Google Scholar 

  9. Myers, R., Wilson, R.C., Hancock, E.R.: Bayesian graph edit distance. IEEE PAMI 22, 628–635 (2000)

    Article  Google Scholar 

  10. Huet, B., Hancock, E.R.: Shape recognition from large image libraries by inexact graph matching. Pattern Recognition Letter 20, 1259–1269 (1999)

    Article  Google Scholar 

  11. Shokoufandeh, A., Dickinson, S., Siddiqi, K., Zucker, S.: Indexing using a spectral encoding of topological structure. In: CVPR, pp. 491–497 (1999)

    Google Scholar 

  12. Scott, G.L., Longuett-Higgins, H.C.: An algorithm for associating the features of two images. Proceedings of the Royal Society of London B 244, 21–26 (1991)

    Article  Google Scholar 

  13. Shapiro, L.S., Brady, J.M.: Feature-based correspondence: an eigenvector approach. Image and Vision Computing 10, 283–288 (1992)

    Article  Google Scholar 

  14. Umeyama, S.: An eigendecomposition approach to weighted graph matching problems. IEEE PAMI 10, 695–703 (1988)

    Article  MATH  Google Scholar 

  15. Carcassoni, M., Hancock, E.R.: Weighted graph-matching using modal clusters. In: Proc. 3rd IAPR-TC15 Workshop Graph-Based Representations in Pattern Recognition, pp. 260–269 (2001)

    Google Scholar 

  16. Caelli, T., Kosinov, S.: An eigenspace projection clustering method for inexact graph matching. IEEE PAMI 26(4), 515–519 (2004)

    Article  Google Scholar 

  17. Bai, X., Yu, H., Hancock, E.R.: Graph matching using spectral embedding and alignment. In: ICPR, vol. 3, pp. 23–26 (2004)

    Google Scholar 

  18. Luo, B., Hancock, E.R.: Structural graph matching using the EM algorithm and singular value decomposition. IEEE PAMI 23, 1120–1136 (2001)

    Article  Google Scholar 

  19. David, J.M., Carey, E.P.: Predicting unobserved links in incompletely observed networks. Computational Statistics & Data Analysis 52, 1373–1386 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Young, S.J., Scheinerman, E.R.: Random Dot Product Graph Models for Social Networks. In: Bonato, A., Chung, F.R.K. (eds.) WAW 2007. LNCS, vol. 4863, pp. 138–149. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  21. Scheinerman, E.R.: Kimberly Tucker. Modeling graphs using dot product representations. Computational Statistics 25(1), 1–16 (2010)

    MathSciNet  Google Scholar 

  22. Zhang, D.M., Sun, D.D., Fu, M.S., Luo, B.: Extended dot product representations of graphs with application to radar image segmentation. Optical Engineering 49(11) (2010)

    Google Scholar 

  23. Bai, X., Latecki, L.J.: Path similarity skeleton graph matching. IEEE PAMI 30(7), 1282–1292 (2008)

    Article  Google Scholar 

  24. Belongie, S., Puzhicha, J., Malik, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE PAMI 24(4), 509–522 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Anhui University, Hefei, 230039, Anhui, China

    Jin Tang, Bo Jiang & Bin Luo

Authors
  1. Jin Tang
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  2. Bo Jiang
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  3. Bin Luo
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Einsteinstraße 62, 48149, Münster, Germany

    Xiaoyi Jiang

  2. Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya, C. Llorens i Artigas 4-6, 08028, Barcelona, Spain

    Miquel Ferrer

  3. Dipartimento di Scienze Ambientali, Informatica, Statistica, Università Ca’ Foscari di Venezia, Via Torino 155, 30172, Venezia Mestre, Italy

    Andrea Torsello

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© 2011 Springer-Verlag Berlin Heidelberg

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Tang, J., Jiang, B., Luo, B. (2011). Graph Matching Based on Dot Product Representation of Graphs. In: Jiang, X., Ferrer, M., Torsello, A. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2011. Lecture Notes in Computer Science, vol 6658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20844-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-20844-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20843-0

  • Online ISBN: 978-3-642-20844-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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