Texture classifiers generated by genetic programming
Andy Song, Vic Ciesielski, Hugh E. Williams · 2003
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.