Applying Genetic Programming to Learn Spatial Differences Between Textures Using A Translation Invariant Representation

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

  author =       "Brian T. Lam and Vic Ciesielski",
  title =        "Applying Genetic Programming to Learn Spatial
                 Differences Between Textures Using A Translation
                 Invariant Representation",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "3",
  pages =        "2202--2209",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554968",
  abstract =     "This paper describes an approach to evolving texture
                 feature extraction programs using tree based genetic
                 programming. The programs are evolved from a learning
                 set of 13 textures selected from the Brodatz database.
                 In the evolutionary phase, texture images are first
                 'binarised' to 256 grey levels. An encoding of the
                 positions of the black pixels is used as the input to
                 the evolved programs. A separate feature extraction
                 program is evolved for each of the 256 grey levels.
                 Fitness is measured by applying the evolved program to
                 all of the images in the learning set, using one
                 dimensional clustering on the outputs and then using
                 the separation between the clusters as the fitness
                 value. On two benchmark problems using the evolved
                 programs for feature extraction and a nearest neighbour
                 classifier, the evolved features gave test accuracies
                 of 74.6percent and 66.2percent respectively for a 13
                 Brodatz and a 15 Vistex texture problem. This is better
                 than a number of human derived methods on the same
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.


Genetic Programming entries for Brian Lam Victor Ciesielski