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Structural Recognition with Kernelized Softassign

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Advances in Artificial Intelligence – IBERAMIA 2004 (IBERAMIA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3315))

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Abstract

In this paper we address the problem of graph matching and graph classification through a kernelized version of the classical Softassign method. Our previous experiments with random-generated graphs have suggested that weighting the Softassign quadratic cost function with distributional information coming from kernel computations on graphs yields a slower decay of matching performance with increasing graph corruption. Here, we test this approach in the context of automatically building graph prototypes and classifying graphs in terms of the distance to the closer prototype. In all cases we use unweighted graphs coming from real images and having sizes from 30 to 140 nodes. Our results show that this approach, consisting on applying graph kernel engineering to matching problems, has a practical use for image classification in terms of pure structural information.

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Authors and Affiliations

  1. Robot Vision Group, Departamento de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Spain

    Miguel Angel Lozano & Francisco Escolano

Authors
  1. Miguel Angel Lozano
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  2. Francisco Escolano
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Editor information

Editors and Affiliations

  1. Laboratorio Nacional de Informatica Avanzada, A.C. Lania, Rebsamen 80, Col. Centro, 91000, Xalapa, Veracruz, Mexico

    Christian Lemaître

  2. Department of Computer Science, National Institute of Astrophysics, Optics and Electronics (INAOE), Luis E. Erro 1, 72840, Sta. Maria Tonantzintla, Puebla, Mexico

    Carlos A. Reyes

  3. Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro 1, 72840, Tonantzintla, México

    Jesús A. González

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Lozano, M.A., Escolano, F. (2004). Structural Recognition with Kernelized Softassign. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_63

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  • DOI: https://doi.org/10.1007/978-3-540-30498-2_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23806-5

  • Online ISBN: 978-3-540-30498-2

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