Genetic Image Network for Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{DBLP:conf/evoW/ShirakawaNN09,

• author = "Shinichi Shirakawa and Shiro Nakayama and Tomoharu Nagao",
• title = "Genetic Image Network for Image Classification",
• booktitle = "Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "5484",
• year = "2009",
• editor = "Mario Giacobini and Anthony Brabazon and Stefano Cagnoni and Gianni A. Di Caro and Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar and Muddassar Farooq and Andreas Fink and Penousal Machado and Jon McCormack and Michael O'Neill and Ferrante Neri and Mike Preuss and Franz Rothlauf and Ernesto Tarantino and Shengxiang Yang",
• pages = "395--404",
• address = "T{\"u}bingen, Germany",
• month = apr # " 15-17",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, image classification, image processing",
• isbn13 = "978-3-642-01128-3",
• size = "10 pages",
• DOI = "doi:10.1007/978-3-642-01129-0_44",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• abstract = "Automatic construction methods for image processing proposed till date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. Genetic Image Network (GIN) is a recent automatic construction method for image processing. The representation of GIN is a network structure. In this paper, we propose a method of automatic construction of image classifiers based on GIN, designated as Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network structure. GIN-IC transforms original images to easier-to-classify images using image transformation nodes, and selects adequate image features using feature extraction nodes. We apply GIN-IC to test problems involving multi-class categorization of texture images, and show that the use of image transformation nodes is effective for image classification problems.",
• notes = "EvoWorkshops2009 held in conjunction with EuroGP2009, EvoCOP2009, EvoBIO2009",

}

Genetic Programming entries for Shinichi Shirakawa Shiro Nakayama Tomoharu Nagao

Citations