Top-Down Evolutionary Image Segmentation Using a Hierarchical Social Metaheuristic

  • Abraham Duarte
  • Ángel Sánchez
  • Felipe Fernández
  • Antonio S. Montemayor
  • Juan J. Pantrigo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3005)


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.


Image Segmentation Winner Strategy Standard Genetic Algorithm Approximate Optimal Solution Pixel Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Abraham Duarte
    • 1
  • Ángel Sánchez
    • 1
  • Felipe Fernández
    • 2
  • Antonio S. Montemayor
    • 1
  • Juan J. Pantrigo
    • 1
  1. 1.ESCET-URJC, Campus de MóstolesMadridSpain
  2. 2.Dept. Tecnología Fotónica, FI-UPMMadridSpain

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