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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

S+SSPR 2018: Structural, Syntactic, and Statistical Pattern Recognition pp 282–292Cite as

Learning the Sub-optimal Graph Edit Distance Edit Costs Based on an Embedded Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11004))

Abstract

Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. One of its main constraints is that it requires an adequate definition of edit costs, which eventually determines which graphs are considered similar. These edit costs are usually defined as concrete functions or constants in a manual fashion and little effort has been done to learn them. The present paper proposes a framework to define these edit costs automatically. Moreover, we concretise this framework in two different models based on neural networks and probability density functions.

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References

  1. Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recogn. Lett. 1(4), 245–253 (1983)

    Article  Google Scholar 

  2. Sanfeliu, A., Fu, K.S.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. 13(3), 353–362 (1983)

    Article  Google Scholar 

  3. Caetano, T., et al.: Learning graph matching. Trans. Pattern Anal. Mach. Intell. 31(6), 1048–1058 (2009)

    Article  Google Scholar 

  4. Leordeanu, M., Sukthankar, R., Hebert, M.: Unsupervised learning for graph matching. Int. J. Comput. Vis. 96(1), 28–45 (2012)

    Article  MathSciNet  Google Scholar 

  5. Cortés, X., Serratosa, F.: Learning graph matching substitution weights based on the ground truth node correspondence. Int. J. Pattern Recogn. Artif. Intell. 30(2), 1650005 (2016). [22 pages]

    Article  MathSciNet  Google Scholar 

  6. Cortés, X., Serratosa, F.: Learning graph-matching edit-costs based on the optimality of the Oracle’s node correspondences. Pattern Recogn. Lett. 56, 22–29 (2015)

    Article  Google Scholar 

  7. Neuhaus, M., Bunke, H.: Automatic learning of cost functions for graph edit distance. Inf. Sci. 177(1), 239–247 (2007)

    Article  MathSciNet  Google Scholar 

  8. Neuhaus, M., Bunke, H.: Self-organizing maps for learning the edit costs in graph matching. IEEE Trans. Syst. Man Cybern. Part B 35(3), 503–514 (2005)

    Article  Google Scholar 

  9. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(7), 950–959 (2009)

    Article  Google Scholar 

  10. Serratosa, F.: Fast computation of bipartite graph matching. Pattern Recogn. Lett. 45, 244–250 (2014)

    Article  Google Scholar 

  11. Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)

    Article  Google Scholar 

  12. Ferrer, M., Serratosa, F., Riesen, K.: Improving bipartite graph matching by assessing the assignment confidence. Pattern Recogn. Lett. 65, 29–36 (2015)

    Article  Google Scholar 

  13. Serratosa, F., Cortés, X.: Interactive graph-matching using active query strategies. Pattern Recogn. 48(4), 1364–1373 (2015)

    Article  Google Scholar 

  14. Luqman, M.M., Ramel, J.-Y., Lladós, J., Brouard, T.: Fuzzy multilevel graph embedding. Pattern Recogn. 46(2), 551–565 (2013)

    Article  Google Scholar 

  15. Moreno-García, C.F., Cortés, X., Serratosa, F.: A graph repository for learning error-tolerant graph matching. In: Robles-Kelly, A., Loog, M., Biggio, B., Escolano, F., Wilson, R. (eds.) S+SSPR 2016. LNCS, vol. 10029, pp. 519–529. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49055-7_46

    Chapter  Google Scholar 

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Acknowledgments

This research is supported by the Spanish projects TIN2016-77836-C2-1-R and ColRobTransp MINECO DPI2016-78957-R AEI/FEDER EU; and also, the European project AEROARMS, H2020-ICT-2014-1-644271.

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Authors and Affiliations

  1. Universitat Rovira i Virgili, Tarragona, Catalonia, Spain

    Pep Santacruz & Francesc Serratosa

Authors
  1. Pep Santacruz
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  2. Francesc Serratosa
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Corresponding author

Correspondence to Francesc Serratosa .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Xiao Bai

  2. University of York, York, United Kingdom

    Edwin R. Hancock

  3. IBM Research – Thomas J. Watson Research, Yorktown Heights, New York, USA

    Tin Kam Ho

  4. University of York, Heslington, York, United Kingdom

    Richard C. Wilson

  5. University of Cagliari, Cagliari, Italy

    Battista Biggio

  6. Data 61 - CSIRO, Canberra, Aust Capital Terr, Australia

    Antonio Robles-Kelly

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Santacruz, P., Serratosa, F. (2018). Learning the Sub-optimal Graph Edit Distance Edit Costs Based on an Embedded Model. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_27

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  • DOI: https://doi.org/10.1007/978-3-319-97785-0_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97784-3

  • Online ISBN: 978-3-319-97785-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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