Genetic programming for evolving figure-ground segmentors from multiple features

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

@Article{Liang:2017:ASC,
  author =       "Yuyu Liang and Mengjie Zhang and Will N. Browne",
  title =        "Genetic programming for evolving figure-ground
                 segmentors from multiple features",
  journal =      "Applied Soft Computing",
  volume =       "51",
  pages =        "83--95",
  year =         "2017",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.07.055",
  URL =          "http://www.sciencedirect.com/science/article/pii/S156849461630391X",
  abstract =     "Figure-ground segmentation is a crucial preprocessing
                 step for many image processing and computer vision
                 tasks. Since different object classes need specific
                 segmentation rules, the top-down approach, which learns
                 from the object information, is more suitable to solve
                 segmentation problems than the bottom-up approach. A
                 problem faced by most existing top-down methods is that
                 they require much human work/intervention, meanwhile
                 introducing human bias. As genetic programming (GP)
                 does not require users to specify the structure of
                 solutions, we apply it to evolve segmentors that can
                 conduct the figure-ground segmentation automatically
                 and accurately. This paper aims to determine what kind
                 of image information is necessary for GP to evolve
                 capable segmentors (especially for images with high
                 variations, e.g. varied object shapes or cluttered
                 backgrounds). Therefore, seven different terminal sets
                 are exploited to evolve segmentors, and images from
                 four datasets (bitmap, Brodatz texture, Weizmann and
                 Pascal databases), which are increasingly difficult for
                 segmentation tasks, are selected for testing. Results
                 show that the proposed GP based method can be
                 successfully applied to diverse types of images. In
                 addition, intensity based features are not sufficient
                 for complex images, whereas features containing
                 spectral and statistical information are necessary.
                 Compared with four widely-used segmentation techniques,
                 our method obtains consistently better segmentation
                 performance.",
  keywords =     "genetic algorithms, genetic programming, Figure-ground
                 segmentation, Intensity based features, Gabor
                 features",
}

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne

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