Towards Genetic Programming for Texture Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Song:2001:TGP,
  author =       "Andy Song and Thomas Loveard and Vic Ciesielski",
  title =        "Towards Genetic Programming for Texture
                 Classification",
  booktitle =    "Proceedings of the 14th International Joint Conference
                 on Artificial Intelligence AI 2001: Advances in
                 Artificial Intelligence",
  volume =       "2256",
  pages =        "461--472",
  year =         "2001",
  editor =       "M. Stumptner and D. Corbett and M. Brooks",
  series =       "Lecture Notes in Computer Science",
  address =      "Adelaide, Australia",
  publisher_address = "Heidelberg",
  month =        dec # " 10-14",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-42960-9",
  CODEN =        "LNCSD9",
  ISSN =         "0302-9743",
  bibdate =      "Fri Mar 8 07:56:44 MST 2002",
  DOI =          "doi:10.1007/3-540-45656-2_40",
  acknowledgement = ack-nhfb,
  abstract =     "The genetic programming (GP) method is proposed as a
                 new approach to perform texture classification based
                 directly on raw pixel data. Two alternative genetic
                 programming representations are used to perform
                 classification. These are dynamic range selection (DRS)
                 and static range selection (SRS). This preliminary
                 study uses four brodatz textures to investigate the
                 applicability of the genetic programming method for
                 binary texture classifications and multi-texture
                 classifications. Results indicate that the genetic
                 programming method, based directly on raw pixel data,
                 is able to accurately classify different textures. The
                 results show that the DRS method is well suited to the
                 task of texture classification. The classifiers
                 generated in our experiments by DRS have good
                 performance over a variety of texture data and offer GP
                 as a promising alternative approach for the difficult
                 problem of texture classification.",
}

Genetic Programming entries for Andy Song Thomas Loveard Victor Ciesielski

Citations