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Evolutionary Techniques for Image Segmentation

  • Karel Mozdren
  • Tomas Burianek
  • Jan Platos
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)

Abstract

Evolutionary algorithms are used in many engineering applications for optimization of problems that are often difficult to solve using conventional methods. One such problem is image segmentation. This task is used for object (contour) extraction from images to create sensible representation of the image. There are many image segmentation and optimization methods. This work is focused on selected evolutionary optimization methods. Namely, particle swarm optimization, genetic algorithm, and differential evolution. Our image segmentation method is inspired in algorithm known as k-means. The optimization function from k-means algorithm is replaced by evolutionary technique. We compare original k-means algorithm with evolutionary approaches and we show that our evolutionary approaches easily outperform the classical approach.

Keywords

particle swarm optimization genetic algorithm differential evolution k-means image segmentation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karel Mozdren
    • 1
  • Tomas Burianek
    • 1
  • Jan Platos
    • 1
  • Václav Snášel
    • 1
  1. 1.Department of Computer ScienceVSB - Technical University of Ostrava, FEECSOstrava-PorubaCzech Republic

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