Applying genetic programming to learn spatial differences between textures using a translation invariant representation

Lam, B and Ciesielski, V 2005, 'Applying genetic programming to learn spatial differences between textures using a translation invariant representation', in D. Corne et al. (ed.) 2005 IEEE Congress On Evolutionary Computation Proceedings, Vol. 3, Edinburgh, UK, 2-5 September 2005, pp. 2202-2209.


Document type: Conference Paper
Collection: Conference Papers

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Title Applying genetic programming to learn spatial differences between textures using a translation invariant representation
Author(s) Lam, B
Ciesielski, V
Year 2005
Conference name IEEE Congress on Evolutionary Computation
Conference location Edinburgh, UK
Conference dates 2-5 September 2005
Proceedings title 2005 IEEE Congress On Evolutionary Computation Proceedings, Vol. 3
Editor(s) D. Corne et al.
Publisher IEEE
Place of publication Piscataway, USA
Start page 2202
End page 2209
Total pages 8
Abstract This paper describes an approach to evolving texture feature extraction programs using tree based genetic programming. The programs are evolved from a learning set of 13 textures selected from the Brodatz database. In the evolutionary phase, texture images are first "binarised" to 256 grey levels. An encoding of the positions of the black pixels is used as the input to the evolved programs. A separate feature extraction program is evolved for each of the 256 grey levels. Fitness is measured by applying the evolved program to all of the images in the learning set, using one dimensional clustering on the outputs and then using the separation between the clusters as the fitness value. On two benchmark problems using the evolved programs for feature extraction and a nearest neighbour classifier, the evolved features gave test accuracies of 74.6% and 66.2% respectively for a 13 Brodatz and a 15 Vistex texture problem. This is better than a number of human derived methods on the same problems.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) genetic programming
DOI - identifier 10.1109/CEC.2005.1554968
Copyright notice © 2005 IEEE
ISBN 0-7803-9363-5
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