Texture Segmentation by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{Song:2008:EC,

• author = "Andy Song and Vic Ciesielski",
• title = "Texture Segmentation by Genetic Programming",
• journal = "Evolutionary Computation",
• year = "2008",
• volume = "16",
• number = "4",
• pages = "461--481",
• month = "Winter",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1063-6560",
• DOI = "doi:10.1162/evco.2008.16.4.461",
• 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.",
• notes = "Part of special issue on Evolutionary Computer Vision \cite{Cagnoni:2008:EC}",

}

Genetic Programming entries for Andy Song Victor Ciesielski

Citations