Texture analysis by genetic programming

Song, A and Ciesielski, V 2004, 'Texture analysis by genetic programming', in G. W. Greenwood (ed.) Proceedings of the 2004 Congress on Evolutionary Computation, Portland, 19-23 June 2004.


Document type: Conference Paper
Collection: Conference Papers

Title Texture analysis by genetic programming
Author(s) Song, A
Ciesielski, V
Year 2004
Conference name Congress on Evolutionary Computation
Conference location Portland
Conference dates 19-23 June 2004
Proceedings title Proceedings of the 2004 Congress on Evolutionary Computation
Editor(s) G. W. Greenwood
Publisher IEEE
Place of publication Piscataway, NJ
Abstract This work presents the use of genetic programming (GP) to a complex domain, texture analysis. Two major tasks of texture analysis, texture classification and texture segmentation, are studied. Bitmap textures are used in this investigation. In classification tasks, the results show that GP is able to evolve accurate classifiers based on texture features. Moreover by using the presented method, GP is able to evolve accurate classifiers without extracting texture features. In texture segmentation tasks, the investigation shows that a fast and accurate segmentation method can be developed based on GP generated texture classifiers. Our further investigation show that the accuracies are not achieved by chance. There are regularities been captured by GP-generated classifiers in performing texture discrimination.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
DOI - identifier 10.1109/CEC.2004.1331154
Copyright notice © 2004 IEEE
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