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International Workshop on Graph-Based Representations in Pattern Recognition

GbRPR 2017: Graph-Based Representations in Pattern Recognition pp 49–58Cite as

Learning Graph Matching with a Graph-Based Perceptron in a Classification Context

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10310))

Abstract

Many tasks in computer vision and pattern recognition are formulated as graph matching problems. Despite the NP-hard nature of the problem, fast and accurate approximations have led to significant progress in a wide range of applications. Learning graph matching functions from observed data, however, still remains a challenging issue. This paper presents an effective scheme to parametrize a graph model for object matching in a classification context. For this, we propose a representation based on a parametrized model graph, and optimize it to increase a classification rate. Experimental evaluations on real datasets demonstrate the effectiveness (in terms of accuracy and speed) of our approach against graph classification with hand-crafted cost functions.

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Author information

Authors and Affiliations

  1. Laboratoire d’Informatique (EA 6300), Université de Tours, 64 avenue Jean Portalis, Tours, France

    Romain Raveaux, Chloé Martineau, Donatello Conte & Gilles Venturini

Authors
  1. Romain Raveaux
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  2. Chloé Martineau
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  3. Donatello Conte
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  4. Gilles Venturini
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Corresponding author

Correspondence to Romain Raveaux .

Editor information

Editors and Affiliations

  1. Università degli Studi di Salerno, Fisciano, Italy

    Pasquale Foggia

  2. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Università degli Studi di Salerno, Fisciano, Italy

    Mario Vento

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Raveaux, R., Martineau, C., Conte, D., Venturini, G. (2017). Learning Graph Matching with a Graph-Based Perceptron in a Classification Context. In: Foggia, P., Liu, CL., Vento, M. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2017. Lecture Notes in Computer Science(), vol 10310. Springer, Cham. https://doi.org/10.1007/978-3-319-58961-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-58961-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58960-2

  • Online ISBN: 978-3-319-58961-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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