Genetic programming as strategy for learning image descriptor operators

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  author =       "Cynthia B. Perez and Gustavo Olague",
  title =        "Genetic programming as strategy for learning image
                 descriptor operators",
  journal =      "Intelligent Data Analysis",
  year =         "2013",
  number =       "4",
  volume =       "17",
  pages =        "561--583",
  keywords =     "genetic algorithms, genetic programming, SIFT, object
                 recognition, F-measure, hill-climbing",
  publisher =    "IOS Press",
  ISSN =         "1088-467X",
  bibdate =      "2013-07-10",
  bibsource =    "DBLP,
  DOI =          "doi:10.3233/IDA-130594",
  size =         "23 pages",
  abstract =     "Nowadays, object recognition based on local invariant
                 features is widely acknowledged as one of the best
                 paradigms for object recognition due to its robustness
                 for solving image matching across different views of a
                 given scene. This paper proposes a new approach for
                 learning invariant region descriptor operators through
                 genetic programming and introduces another optimisation
                 method based on a hill-climbing algorithm with multiple
                 re-starts. The approach relies on the synthesis of
                 mathematical expressions that extract information
                 derived from local image patches called local features.
                 These local features have been previously designed by
                 human experts using traditional representations that
                 have a clear and, preferably mathematically,
                 well-founded definition. We propose in this paper that
                 the mathematical principles that are used in the
                 description of such local features could be well
                 optimised using a genetic programming paradigm.
                 Experimental results confirm the validity of our
                 approach using a widely accepted testbed that is used
                 for testing local descriptor algorithms. In addition,
                 we compare our results not only against three
                 state-of-the-art algorithms designed by human experts,
                 but also, against a simpler search method for
                 automatically generating programs such as hill-climber.
                 Furthermore, we provide results that illustrate the
                 performance of our improved SIFT algorithms using an
                 object recognition application for indoor and outdoor

Genetic Programming entries for Cynthia B Perez Gustavo Olague