Texture Segmentation by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Andy Song and Vic Ciesielski",
  title =        "Texture Segmentation by Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2008",
  volume =       "16",
  number =       "4",
  pages =        "461--481",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/evco.2008.16.4.461",
  abstract =     "This paper describes a texture segmentation method
                 using genetic programming (GP), which is one of the
                 most powerful evolutionary computation algorithms. By
                 choosing an appropriate representation texture,
                 classifiers can be evolved without computing texture
                 features. Due to the absence of time-consuming feature
                 extraction, the evolved classifiers enable the
                 development of the proposed texture segmentation
                 algorithm. This GP based method can achieve a
                 segmentation speed that is significantly higher than
                 that of conventional methods. This method does not
                 require a human expert to manually construct models for
                 texture feature extraction. In an analysis of the
                 evolved classifiers, it can be seen that these GP
                 classifiers are not arbitrary. Certain textural
                 regularities are captured by these classifiers to
                 discriminate different textures. GP has been shown in
                 this study as a feasible and a powerful approach for
                 texture classification and segmentation, which are
                 generally considered as complex vision tasks.",
  notes =        "Part of special issue on Evolutionary Computer Vision

Genetic Programming entries for Andy Song Victor Ciesielski