Distribution-based invariant feature construction using genetic programming for edge detection

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

@Article{Fu:2015:SC,
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Distribution-based invariant feature construction
                 using genetic programming for edge detection",
  journal =      "Soft Computing",
  year =         "2015",
  volume =       "19",
  number =       "8",
  pages =        "2371--2389",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, SVM, Edge
                 detection, Distribution estimation, Feature
                 extraction",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-014-1432-4",
  size =         "19 pages",
  abstract =     "In edge detection, constructing features with rich
                 responses on different types of edges is a challenging
                 problem. Genetic programming (GP) has been previously
                 employed to construct features. Normally, the values of
                 the features constructed by GP are calculated from raw
                 observations. Some existing work has considered the
                 distributions of the raw observations, but these
                 features only poorly indicate class label
                 probabilities. To construct features with rich
                 responses on different types of edges, the
                 distributions of the observations from GP programs are
                 investigated in this study. The values of the
                 constructed features are obtained from estimated
                 distributions, rather than directly using the
                 observations. These features themselves indicate
                 probabilities for the target labels. Basic
                 rotation-invariant features from gradients, image
                 quality, and local histograms are used to construct new
                 composite features. The results show that the invariant
                 features constructed by GP combine advantages from the
                 basic features, reduce drawbacks from basic features
                 alone, and improve the detection performance. In terms
                 of the quantitative and qualitative evaluations,
                 features constructed by GP with distribution estimation
                 are better than the combinations from a Bayesian model
                 and a linear support vector machine approach.",
}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations