Ant colony optimization for image regularization based on a nonstationary markov modeling

  • Le Hégarat-Mascle S
  • Kallel A
  • Descombes X
  • 24


    Mendeley users who have this article in their library.
  • 28


    Citations of this article.


Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The 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 the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixed-form neighborhood used in classical Markov random field regularization techniques. The performance of this new approach is illustrated on a simulated image and on actual remote sensing images.

Author-supplied keywords

  • Ant colony
  • Classification
  • Image model
  • Markov random field (MRF)

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free