Applying Genetic Programming to Learn Spatial Differences Between Textures Using A Translation Invariant Representation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{lam:2005:CEC,
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author = "Brian T. Lam and Vic Ciesielski",
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title = "Applying Genetic Programming to Learn Spatial
Differences Between Textures Using A Translation
Invariant Representation",
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booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
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year = "2005",
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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",
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volume = "3",
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pages = "2202--2209",
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address = "Edinburgh, UK",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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month = "2-5 " # sep,
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organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7803-9363-5",
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DOI = "doi:10.1109/CEC.2005.1554968",
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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.",
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notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.
whereiswaldo",
- }
Genetic Programming entries for
Brian Lam
Victor Ciesielski
Citations