title: Texture segmentation by genetic programming.
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.
authors.affiliation: School of Computer Science and Information Technology, RMIT University, Melbourne Victoria, Australia. andy.song@rmit.edu.au
authors.forename: Andy
authors.initials: A
authors.lastname: Song
authors.forename: Vic
authors.initials: V
authors.lastname: Ciesielski
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003201:Computers; D007091:Image Processing, Computer-Assisted; D008957:Models, Genetic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012984:Software; D013997:Time Factors; D014785:Vision, Ocular