• DocumentCode
    3529342
  • Title

    Improved genetic neural network for image segmentation

  • Author

    Wang, Qing-sheng ; Zhang, Yue-qin ; Hu, Bin ; Zhao, Jian-guang

  • Author_Institution
    Comput. Sci. & Technol. Inst., Taiyuan Univ. of Technol., Taiyuan, China
  • Volume
    Part 3
  • fYear
    2011
  • fDate
    3-5 Sept. 2011
  • Firstpage
    1694
  • Lastpage
    1698
  • Abstract
    The paper provides the method of the improved genetic neural network for image segmentation. The method uses improved genetic algorithm BP neural network weights and thresholds to optimize, and use the definition of bipolar fitness function mapping compression to speed up neural network training speed, and then use iterative improved neural network algorithm to achieve image segmentation. The results of experimental show that the improved genetic neural network can better achieve the image segmentation, compared with the traditional method; Compared with BP neural network training speed is greatly improved.
  • Keywords
    backpropagation; genetic algorithms; image segmentation; iterative methods; neural nets; bipolar fitness function mapping compression; image segmentation; improved genetic algorithm BP neural network; iterative improved neural network algorithm; Arrays; Genetic algorithms; Genetics; Image segmentation; Simulation; Support vector machine classification; Training; Genetic neural Network; Image segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management (IE&EM), 2011 IEEE 18Th International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-61284-446-6
  • Type

    conf

  • DOI
    10.1109/ICIEEM.2011.6035488
  • Filename
    6035488