Learning Figure-ground Image Segmentors by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

@InProceedings{Liang:2017:GECCO,
  author =       "Yuyu Liang and Mengjie Zhang and Will N. Browne",
  title =        "Learning Figure-ground Image Segmentors by Genetic
                 Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "239--240",
  size =         "2 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3075989",
  DOI =          "doi:10.1145/3067695.3075989",
  acmid =        "3075989",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, figure-ground
                 segmentation, postprocessing, preprocessing",
  month =        "15-19 " # jul,
  abstract =     "Figure-ground segmentation is an important image
                 processing task that genetic programming (GP) has been
                 successfully introduced to solve. However, existing GP
                 methods use a homogeneous mixture of preprocessing and
                 post processing operators for segmentation. This can
                 result in inappropriate operators being connected,
                 leading to poor performance and unnecessary operations
                 in solutions. To address this issue, two new methods
                 are designed to enable GP to conduct image
                 preprocessing, binarisation and postprocessing
                 separately. Specifically, the two methods introduce a
                 strongly-typed representation (StronglyGP) and a
                 two-stage evolution (TwostageGP) in GP respectively
                 Results show that StronglyGP can evolve effective
                 segmentors for the given complex segmentation tasks.
                 However, TwostageGP currently performs poorly, which is
                 likely caused by over fitting, which will be addressed
                 in future work.",
  notes =        "Also known as \cite{Liang:2017:LFI:3067695.3075989}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne

Citations