A Supervised Figure-ground Segmentation Method using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Liang:2015:evoApplications,
  author =       "Yuyu Liang and Mengjie Zhang and Will Browne",
  title =        "A Supervised Figure-ground Segmentation Method using
                 Genetic Programming",
  booktitle =    "18th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2015",
  editor =       "Antonio M. Mora and Giovanni Squillero",
  series =       "LNCS",
  volume =       "9028",
  publisher =    "Springer",
  pages =        "491--503",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Image
                 segmentation, Raw pixel values, Grayscale statistics",
  isbn13 =       "978-3-319-16548-6",
  DOI =          "doi:10.1007/978-3-319-16549-3_40",
  abstract =     "Figure-ground segmentation is an important
                 preprocessing phase in many computer vision
                 applications. As different classes of objects require
                 specific segmentation rules, supervised (or top-down)
                 methods, which learn from prior knowledge of objects,
                 are suitable for figure-ground segmentation. However,
                 existing top-down methods, such as model-based and
                 fragment-based ones, involve a lot of human work. As
                 genetic programming (GP) can evolve computer programs
                 to solve problems automatically, it requires less human
                 work. Moreover, since GP contains little human bias, it
                 is possible for GP-evolved methods to obtain better
                 results than human constructed approaches. This paper
                 develops a supervised GP-based segmentation system.
                 Three kinds of simple features, including raw pixel
                 values, six dimension and eleven dimension grayscale
                 statistics, are employed to evolve image segmentors.
                 The evolved segmentors are tested on images from four
                 databases with increasing difficulty, and results are
                 compared with four conventional techniques including
                 thresholding, region growing, clustering, and active
                 contour models. The results show that GP-evolved
                 segmentors perform better than the four traditional
                 methods with consistently good results on both simple
                 and complex images.",
  notes =        "EvoIASP EvoApplications2015 held in conjunction with
                 EuroGP'2015, EvoCOP2015 and EvoMusArt2015
                 http://www.evostar.org/2015/cfp_evoapps.php",
}

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne

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