Title :
Texture classifiers generated by genetic programming
Author :
Song, Andy ; Ciesielski, Vic ; Williams, Hugh E.
Author_Institution :
Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, Vic., Australia
Abstract :
We investigate the behaviour of image texture classifiers generated by genetic programming. We propose techniques to understand how classifiers capture textural characteristics and for discussing the effectiveness of different classifiers. Our results show that regularities of patterns can be detected by the genetic programming method without predefined knowledge
Keywords :
genetic algorithms; image classification; image texture; experiments; genetic programming; image classification; image texture classifiers; pattern regularities; textural characteristics; Arithmetic; Australia; Computer science; Data mining; Genetic programming; Image generation; Image texture; Information technology; Pixel; Problem-solving;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1006241
