• DocumentCode
    2340260
  • Title

    Ant colony optimization for image segmentation

  • Author

    Wang, Xiao-Nian ; Feng, Yuan-Jing ; Feng, Zu-Ren

  • Author_Institution
    Syst. Eng. Inst., Xi´´an Jiao Tong Univ., China
  • Volume
    9
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    5355
  • Abstract
    It is found that the multistage decision algorithm for image segmentation with active contour model (ACM) is similar to ant colony optimization (ACO). By means of constructing solution space and heuristic information, a new algorithm based on ACM is proposed in the paper, which uses ACO to search for the best path in a constrained region. This algorithm that provides a new approach to obtain precise contour, is proved to be convergent with probability one, and will reach the best feasible boundary with minimum energy function value. Moreover, this algorithm can also be used to solve other revised ACM problems. The simulation results show that the proposed approach is more effective than the genetic algorithm in literature (Mishraa et al., 2003).
  • Keywords
    genetic algorithms; image segmentation; active contour model; ant colony optimization; constrained region; genetic algorithm; heuristic information; image segmentation; multistage decision algorithm; Active contours; Ant colony optimization; Computational modeling; Equations; Genetic algorithms; Greedy algorithms; Image converters; Image segmentation; Optimization methods; Systems engineering and theory; Active Contour Model; Ant Colony Optimization; Image segment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527890
  • Filename
    1527890