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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{lam:2005:CEC,

• 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 Computation",
• 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 problems.",
• notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.

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}

Genetic Programming entries for Brian Lam Victor Ciesielski

Citations