Genetic Image Network for Image Classification

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@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",
  URL =          "http://www.springerlink.com/content/r0722q3444788837/",
  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