Texture Segmentation by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Song:2008:EC,
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author = "Andy Song and Vic Ciesielski",
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title = "Texture Segmentation by Genetic Programming",
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journal = "Evolutionary Computation",
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year = "2008",
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volume = "16",
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number = "4",
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pages = "461--481",
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month = "Winter",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/evco.2008.16.4.461",
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abstract = "This paper describes a texture segmentation method
using genetic programming (GP), which is one of the
most powerful evolutionary computation algorithms. By
choosing an appropriate representation texture,
classifiers can be evolved without computing texture
features. Due to the absence of time-consuming feature
extraction, the evolved classifiers enable the
development of the proposed texture segmentation
algorithm. This GP based method can achieve a
segmentation speed that is significantly higher than
that of conventional methods. This method does not
require a human expert to manually construct models for
texture feature extraction. In an analysis of the
evolved classifiers, it can be seen that these GP
classifiers are not arbitrary. Certain textural
regularities are captured by these classifiers to
discriminate different textures. GP has been shown in
this study as a feasible and a powerful approach for
texture classification and segmentation, which are
generally considered as complex vision tasks.",
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notes = "Part of special issue on Evolutionary Computer Vision
\cite{Cagnoni:2008:EC}",
- }
Genetic Programming entries for
Andy Song
Victor Ciesielski
Citations