Discovery of human-competitive image texture feature extraction programs using genetic programming

Lam, B and Ciesielski, V 2004, 'Discovery of human-competitive image texture feature extraction programs using genetic programming', in K. Deb et al. (ed.) Genetic and Evolutionary Computation - GECCO 2004, Seattle, 1 June 2004.


Document type: Conference Paper
Collection: Conference Papers

Title Discovery of human-competitive image texture feature extraction programs using genetic programming
Author(s) Lam, B
Ciesielski, V
Year 2004
Conference name Annual Genetic and Evolutionary Computation
Conference location Seattle
Conference dates 1 June 2004
Proceedings title Genetic and Evolutionary Computation - GECCO 2004
Editor(s) K. Deb et al.
Publisher Springer
Place of publication Berlin
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 74.8% while using Haralick features, the most commonly used method in texture classification, gave an accuracy of 75.5% 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 83.2%, indicating that the evolved features are finding texture regularities not used in the Haralick approach.
Subjects Neural, Evolutionary and Fuzzy Computation
Keyword(s) genetic programming
texture feature extraction algorithms
Copyright notice © Springer-Verlag Berlin Heidelberg 2003
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