Texture segmentation by genetic programming

Song, A and Ciesielski, V 2008, 'Texture segmentation by genetic programming', Evolutionary Computation, vol. 16, no. 4, pp. 461-481.


Document type: Journal Article
Collection: Journal Articles

Title Texture segmentation by genetic programming
Author(s) Song, A
Ciesielski, V
Year 2008
Journal name Evolutionary Computation
Volume number 16
Issue number 4
Start page 461
End page 481
Total pages 21
Publisher MIT Press
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.
Subject Applied Mathematics not elsewhere classified
Keyword(s) machine vision
image classification
texture classification
texture segmentation
genetic programming
evolutionary computation
machine learning
DOI - identifier 10.1162/evco.2008.16.4.461
Copyright notice © 2008 by the Massachusetts Institute of Technology.
ISSN 1063-6560
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