Figure-ground Image Segmentation using Genetic Programming and Feature Selection

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

  author =       "Yuyu Liang and Mengjie Zhang and Will N. Browne",
  title =        "Figure-ground Image Segmentation using Genetic
                 Programming and Feature Selection",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3839--3846",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744276",
  abstract =     "Figure-ground segmentation is an essential but
                 difficult preprocessing step for many computer vision
                 and image preprocessing tasks, such as object
                 recognition. One challenge is to separate objects from
                 backgrounds on images with high variations (e.g. in
                 object shapes), which requires both effective feature
                 sets and powerful segmentors. This paper develops a GP
                 based segmentation method, which transforms
                 segmentation tasks into pixel classification based
                 problems. To control the complexity of evolved
                 solutions, parsimony pressure is introduced in GP.
                 Tested on two datasets with high variations (the
                 Weizmann and Pascal datasets), the proposed method
                 achieves similar performance in F1 score with much
                 simpler solutions, compared with a reference GP based
                 method that does not consider solution complexity.
                 Moreover, it is the first time that the occurrence
                 rates of the features used by the evolved solutions are
                 studied to conduct feature selection for figure-ground
                 segmentation. Compared with the whole feature set using
                 traditional classifier based segmentation methods, the
                 selected feature subsets can improve the segmentation
                 performance. Moreover, analyses on the evolved
                 solutions reveal how they function and why specific
                 features are selected.",
  notes =        "WCCI2016",

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne