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Top-Down Evolutionary Image Segmentation Using a Hierarchical Social Metaheuristic

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Applications of Evolutionary Computing (EvoWorkshops 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3005))

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Abstract

This paper presents an application of a hierarchical social (HS) metaheuristic to region-based segmentation. The original image is modelled as a simplified image graph, which is successively partitioned into two regions, corresponding to the most significant components of the actual image, until a termination condition is met. The graph-partitioning task is solved as a variant of the min-cut problem (normalized cut) using an HS metaheuristic. The computational efficiency of the proposed algorithm for the normalized cut computation improves the performance of a standard genetic algorithm. We applied the HS approach to brightness segmentation on various synthetic and real images, with stimulating trade-off results between execution time and segmentation quality.

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Author information

Authors and Affiliations

  1. ESCET-URJC, Campus de Móstoles, 28933, Madrid, Spain

    Abraham Duarte, Ángel Sánchez, Antonio S. Montemayor & Juan J. Pantrigo

  2. Dept. Tecnología Fotónica, FI-UPM, Campus de Montegancedo, 28860, Madrid, Spain

    Felipe Fernández

Authors
  1. Abraham Duarte
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  2. Ángel Sánchez
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  3. Felipe Fernández
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  4. Antonio S. Montemayor
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  5. Juan J. Pantrigo
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Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  3. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  10. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2004 Springer-Verlag Berlin Heidelberg

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Duarte, A., Sánchez, Á., Fernández, F., Montemayor, A.S., Pantrigo, J.J. (2004). Top-Down Evolutionary Image Segmentation Using a Hierarchical Social Metaheuristic. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_31

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive

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