Evolving Texture Features by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Melanie Aurnhammer",
  title =        "Evolving Texture Features by Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP},
                 {EvoInteraction}, {EvoMUSART}, {EvoSTOC},
  year =         "2007",
  month =        "11-13 " # apr,
  editor =       "Mario Giacobini and Anthony Brabazon and 
                 Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and 
                 Muddassar Farooq and Andreas Fink and 
                 Evelyne Lutton and Penousal Machado and Stefan Minner and 
                 Michael O'Neill and Juan Romero and Franz Rothlauf and 
                 Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and 
                 Shengxiang Yang",
  series =       "LNCS",
  volume =       "4448",
  publisher =    "Springer Verlag",
  address =      "Valencia, Spain",
  pages =        "351--358",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-71804-8",
  DOI =          "doi:10.1007/978-3-540-71805-5_38",
  abstract =     "Feature extraction is a crucial step for Computer
                 Vision applications. Finding appropriate features for
                 an application often means hand-crafting task specific
                 features with many parameters to tune. A generalisation
                 to other applications or scenarios is in many cases not
                 possible. Instead of engineering features, we describe
                 an approach which uses Genetic Programming to generate
                 features automatically. In addition, we do not
                 predefine the dimension of the feature vector but
                 pursue an iterative approach to generate an appropriate
                 number of features. We present this approach on the
                 problem of texture classification based on
                 co-occurrence matrices. Our results are compared to
                 those obtained by using seven Haralick texture
                 features, as well as results reported in the literature
                 on the same database. Our approach yielded a
                 classification performance of up to 87percent which is
                 an improvement of 30percent over the Haralick features.
                 We achieved an improvement of 12percent over previously
                 reported results while reducing the dimension of the
                 feature vector from 78 to four.",
  notes =        "EvoWorkshops2007",

Genetic Programming entries for Melanie Aurnhammer