Discovery of Human-Competitive Image Texture Feature Extraction Programs Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Brian Lam and Vic Ciesielski",
  title =        "Discovery of Human-Competitive Image Texture Feature
                 Extraction Programs Using Genetic Programming",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part II",
  year =         "2004",
  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",
  series =       "Lecture Notes in Computer Science",
  pages =        "1114--1125",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3103",
  ISBN =         "3-540-22343-6",
  ISSN =         "0302-9743",
  URL =          "",
  DOI =          "doi:10.1007/b98645",
  size =         "12",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In this paper 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.",
  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