Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7325
- @InProceedings{lam:doh:gecco2004,
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author = "Brian Lam and Vic Ciesielski",
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title = "Discovery of Human-Competitive Image Texture Feature
Extraction Programs Using Genetic Programming",
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booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
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year = "2004",
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editor = "Kalyanmoy Deb and Riccardo Poli and
Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and
Paul Darwen and Dipankar Dasgupta and Dario Floreano and
James Foster and Mark Harman and Owen Holland and
Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and
Dirk Thierens and Andy Tyrrell",
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series = "Lecture Notes in Computer Science",
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pages = "1114--1125",
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address = "Seattle, WA, USA",
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publisher_address = "Heidelberg",
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month = "26-30 " # jun,
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organisation = "ISGEC",
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publisher = "Springer-Verlag",
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volume = "3103",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-22343-6",
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ISSN = "0302-9743",
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URL = "
http://www.genetic-programming.org/gecco2004hc/lam-paper.pdf",
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URL = "
https://researchrepository.rmit.edu.au/esploro/outputs/9921863828201341",
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DOI = "
doi:10.1007/978-3-540-24855-2_121",
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size = "12 pages",
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abstract = "we show how genetic programming can be used to
discover useful texture feature extraction algorithms.
Grey level histograms of different textures are used as
inputs to the evolved programs. One dimensional K-means
clustering is applied to the outputs and the tightness
of the clusters is used as the fitness measure. To test
generality, textures from the Brodatz library were used
in learning phase and the evolved features were used on
classification problems based on the Vistex library.
Using the evolved features gave a test accuracy of
74.8percent while using Haralick features, the most
commonly used method in texture classification, gave an
accuracy of 75.5percent on the same problem. Thus, the
evolved features are competitive with those derived by
human intuition and analysis. Furthermore, when the
evolved features are combined with the Haralick
features the accuracy increases to 83.2percent,
indicating that the evolved features are finding
texture regularities not used in the Haralick
approach.",
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notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
- }
Genetic Programming entries for
Brian Lam
Victor Ciesielski
Citations