Visual Learning by Evolutionary and Coevolutionary Feature Synthesis

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

  author =       "Krzysztof Krawiec and Bir Bhanu",
  title =        "Visual Learning by Evolutionary and Coevolutionary
                 Feature Synthesis",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2007",
  volume =       "11",
  number =       "5",
  pages =        "635--650",
  month =        oct,
  email =        "",
  keywords =     "genetic algorithms, genetic programming, pattern
                 recognition, visual learning, cooperative coevolution",
  URL =          "",
  DOI =          "doi:10.1109/TEVC.2006.887351",
  size =         "16 pages",
  abstract =     "In this paper, we present a novel method for learning
                 complex concepts/hypotheses directly from raw training
                 data. The task addressed here concerns data-driven
                 synthesis of recognition procedures for real-world
                 object recognition. The method uses linear genetic
                 programming to encode potential solutions expressed in
                 terms of elementary operations, and handles the
                 complexity of the learning task by applying cooperative
                 coevolution to decompose the problem automatically at
                 the genotype level. The training coevolves feature
                 extraction procedures, each being a sequence of
                 elementary image processing and computer vision
                 operations applied to input images. Extensive
                 experimental results show that the approach attains
                 competitive performance for {3D} object recognition in
                 real synthetic aperture radar (SAR) imagery.",

Genetic Programming entries for Krzysztof Krawiec Bir Bhanu