• DocumentCode
    1651990
  • Title

    Texture classifiers generated by genetic programming

  • Author

    Song, Andy ; Ciesielski, Vic ; Williams, Hugh E.

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, Vic., Australia
  • Volume
    1
  • fYear
    2002
  • Firstpage
    243
  • Lastpage
    248
  • Abstract
    We investigate the behaviour of image texture classifiers generated by genetic programming. We propose techniques to understand how classifiers capture textural characteristics and for discussing the effectiveness of different classifiers. Our results show that regularities of patterns can be detected by the genetic programming method without predefined knowledge
  • Keywords
    genetic algorithms; image classification; image texture; experiments; genetic programming; image classification; image texture classifiers; pattern regularities; textural characteristics; Arithmetic; Australia; Computer science; Data mining; Genetic programming; Image generation; Image texture; Information technology; Pixel; Problem-solving;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
  • Conference_Location
    Honolulu, HI
  • Print_ISBN
    0-7803-7282-4
  • Type

    conf

  • DOI
    10.1109/CEC.2002.1006241
  • Filename
    1006241
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1651990