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Sub-optimal Graph Matching by Node-to-Node Assignment Classification

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Graph-Based Representations in Pattern Recognition (GbRPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11510))

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Abstract

In the recent years, Graph Edit Distance has awaken interest in the scientific community and some new graph-matching algorithms that compute it have been presented. Nevertheless, these algorithms usually cannot be used in real applications due to runtime restrictions. For this reason, other graph-matching algorithms have also been used that compute an approximation of the graph correspondence with lower runtime. Clearly, in a real application, there is a tradeoff between runtime and accuracy. One of the most costly part in these algorithms is the deduction of the node-to-node mapping. We present a new graph-matching algorithm that returns a graph correspondence without the explicit computation of the assignment problem. This is done thanks to a classification of the node-to-node assignment learned in a previous training stage.

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Acknowledgements

This research is supported by projects TIN2016-77836-C2-1-R and DPI2016-78957-R and AEROARMS (H2020-ICT-2014-1-644271 (Spain), and by DANIEAL2 project of Centre-Val-de-Loire Region (France).

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Authors and Affiliations

  1. Laboratoire d’Informatique Fondamentale et Appliquée de Tours (LIFAT - EA6300), Tours, France

    Xavier Cortés & Donatello Conte

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

    Francesc Serratosa

Authors
  1. Xavier Cortés
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  2. Donatello Conte
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  3. Francesc Serratosa
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Corresponding author

Correspondence to Xavier Cortés .

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Editors and Affiliations

  1. University of Tours, Tours, France

    Donatello Conte

  2. University of Tours, Tours, France

    Jean-Yves Ramel

  3. University of Salerno, Fisicano, Italy

    Pasquale Foggia

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Cortés, X., Conte, D., Serratosa, F. (2019). Sub-optimal Graph Matching by Node-to-Node Assignment Classification. In: Conte, D., Ramel, JY., Foggia, P. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2019. Lecture Notes in Computer Science(), vol 11510. Springer, Cham. https://doi.org/10.1007/978-3-030-20081-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-20081-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20080-0

  • Online ISBN: 978-3-030-20081-7

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