Evolutionary feature synthesis for object recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  title =        "Evolutionary feature synthesis for object
  author =       "Yingqiang Lin and Bir Bhanu",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics,
                 Part C: Applications and Reviews",
  year =         "2005",
  volume =       "35",
  number =       "2",
  pages =        "156--171",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, feature
                 extraction, object recognition, radar imaging,
                 synthetic aperture radar, SAR images, coevolutionary
                 genetic programming approach, domain-independent
                 primitive operator, evolutionary feature synthesis,
                 human experts, object recognition, real synthetic
                 aperture radar, vehicle recognition",
  DOI =          "doi:10.1109/TSMCC.2004.841912",
  ISSN =         "1094-6977",
  abstract =     "Features represent the characteristics of objects and
                 selecting or synthesising effective composite features
                 are the key to the performance of object recognition.
                 In this paper, we propose a coevolutionary genetic
                 programming (CGP) approach to learn composite features
                 for object recognition. The knowledge about the problem
                 domain is incorporated in primitive features that are
                 used in the synthesis of composite features by CGP
                 using domain-independent primitive operators. The
                 motivation for using CGP is to overcome the limitations
                 of human experts who consider only a small number of
                 conventional combinations of primitive features during
                 synthesis. CGP, on the other hand, can try a very large
                 number of unconventional combinations and these
                 unconventional combinations yield exceptionally good
                 results in some cases. Our experimental results with
                 real synthetic aperture radar (SAR) images show that
                 CGP can discover good composite features to distinguish
                 objects from clutter and to distinguish among objects
                 belonging to several classes. The comparison with other
                 classical classification algorithms is favourable to
                 the CGP-based approach proposed in this paper.",

Genetic Programming entries for Yingqiang Lin Bir Bhanu