Evolving estimators of the pointwise Hoelder exponent with Genetic Programming

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@Article{Trujillo201261,
  author =       "Leonardo Trujillo and Pierrick Legrand and 
                 Gustavo Olague and Jacques Levy-Vehel",
  title =        "Evolving estimators of the pointwise Hoelder exponent
                 with Genetic Programming",
  title2 =       "Evolving estimators of the pointwise Holder exponent
                 with Genetic Programming",
  journal =      "Information Sciences",
  volume =       "209",
  pages =        "61--79",
  year =         "2012",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2012.04.043",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025512003386",
  URL =          "http://hal.inria.fr/hal-00643387",
  URL =          "http://hal.inria.fr/docs/00/64/33/87/PDF/INS-S-11-01794-extrait.pdf",
  language =     "ENG",
  oai =          "oai:hal.inria.fr:hal-00643387",
  keywords =     "genetic algorithms, genetic programming, Hoelder
                 regularity, Local image description",
  abstract =     "The regularity of a signal can be numerically
                 expressed using Hoelder exponents, which characterise
                 the singular structures a signal contains. In
                 particular, within the domains of image processing and
                 image understanding, regularity-based analysis can be
                 used to describe local image shape and appearance.
                 However, estimating the Hoelder exponent is not a
                 trivial task, and current methods tend to be
                 computationally slow and complex. This work presents an
                 approach to automatically synthesise estimators of the
                 pointwise Hoelder exponent for digital images. This
                 task is formulated as an optimisation problem and
                 Genetic Programming (GP) is used to search for
                 operators that can approximate a traditional estimator,
                 the oscillations method. Experimental results show that
                 GP can generate estimators that achieve a low error and
                 a high correlation with the ground truth estimation.
                 Furthermore, most of the GP estimators are faster than
                 traditional approaches, in some cases their run time is
                 orders of magnitude smaller. This result allowed us to
                 implement a real-time estimation of the Hoelder
                 exponent on a live video signal, the first such
                 implementation in current literature. Moreover, the
                 evolved estimators are used to generate local
                 descriptors of salient image regions, a task for which
                 a stable and robust matching is achieved, comparable
                 with state-of-the-art methods. In conclusion, the
                 evolved estimators produced by GP could help expand the
                 application domain of Hoelder regularity within the
                 fields of image analysis and signal processing.",
  notes =        "Entered for 2013 HUMIES GECCO 2013",
}

Genetic Programming entries for Leonardo Trujillo Pierrick Legrand Gustavo Olague Jacques Levy-Vehel

Citations