Fast texture segmentation using genetic programming

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

@InProceedings{song:2003:ftsugp,
  author =       "Andy Song and Vic Ciesielski",
  title =        "Fast texture segmentation using genetic programming",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "2126--2133",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Australia,
                 Computer science, Dynamic range, Feature extraction,
                 Image segmentation, Image texture analysis, Information
                 technology, Partitioning algorithms, Voting, image
                 segmentation, image texture, complex domain, fast
                 segmentation, pixel values, texture classifiers,
                 texture region partitioning, texture segmentation,
                 voting strategy",
  URL =          "http://goanna.cs.rmit.edu.au/~vc/papers/cec2003-song.pdf",
  DOI =          "doi:10.1109/CEC.2003.1299935",
  ISBN =         "0-7803-7804-0",
  abstract =     "We extend genetic programming (GP) to texture
                 segmentation. By this method, segmentation tasks are
                 performed by texture classifiers which are evolved by
                 GP. Small cutouts sampled from images of various
                 textures are used for the evolution. The generated
                 classifiers directly use pixel values as input. Based
                 on these classifiers an algorithm which uses a voting
                 strategy to partition texture regions is developed.

                 The results of the investigation indicate that the
                 proposed method is able to accurately identify the
                 boundaries between different texture regions, even if
                 the boundaries are not regular. The method can segment
                 two textures as well as multiple textures. Furthermore
                 fast segmentation can be achieved. The speed of the
                 proposed texture segmentation method can be a hundred
                 times faster than conventional methods.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",
}

Genetic Programming entries for Andy Song Victor Ciesielski

Citations