• DocumentCode
    507824
  • Title

    Image Segmentation Based on Local Ant Colony Optimization

  • Author

    Zou, Ruobing ; Yu, Weiyu ; Yu, Zhiding ; Yu, Xiangyu

  • Author_Institution
    Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
  • Volume
    3
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    35
  • Lastpage
    39
  • Abstract
    In this paper, we proposed an improved image binary segmentation based Ant Colony Algorithm. Within different image areas, different iteration numbers and steps have been set for ants, achieving superior image segmentation results. Experimental results indicate the proposed method can enhance segmentation accuracy and reduce running time, thus possessing considerable application potential.
  • Keywords
    artificial life; fuzzy set theory; image classification; image segmentation; optimisation; pattern clustering; fuzzy C-means clustering; image area; image binary segmentation; image pixel classification; iteration number; local ant colony optimization; Ant colony optimization; Application software; Clustering algorithms; Computer vision; Image analysis; Image processing; Image segmentation; Lighting; Neural networks; Pixel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.647
  • Filename
    5363299
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=507824