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Use of Ant Colony Optimization for Finding Neighborhoods in Image Non-stationary Markov Random Field Classification

  • Sylvie Le Hégarat-Mascle
  • Abdelaziz Kallel
  • Xavier Descombes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

Abstract

In global classifications using Markov Random Field (MRF) modeling, the neighborhood form is generally considered as independent of its location in the image. Such an approach may lead to classification errors for pixels located at the segment borders. The solution proposed here consists in relaxing the assumption of fixed-form neighborhood. Here we propose to use the Ant Colony Optimization (ACO) and to exploit its ability of self-organization. Modeling upon the behavior of social insects for computing strategies, the ACO ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within a same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form. Performance of this new approach is illustrated on a simulated image and on actual remote sensing images SPOT4/HRV.

Keywords

Markov Random Fields Label Image Find Neighborhood Markov Random Fields Modeling Pixel Label 
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 2007

Authors and Affiliations

  • Sylvie Le Hégarat-Mascle
    • 1
  • Abdelaziz Kallel
    • 2
  • Xavier Descombes
    • 3
  1. 1.IEF/AXIS, Université de Paris-Sud 91405, Orsay CedexFrance
  2. 2.CETP/IPSL, 10, 12 avenue de l’Europe 78140, VélizyFrance
  3. 3.CNRS/INRIA/UNSA, INRIA, 06902 Sophia Antipolis, CedexFrance

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