Genetic Programming for Object Detection: A Two-Phase Approach with an Improved Fitness Function

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

@Article{Zhang:2006:ELCVIA,
  author =       "Mengjie Zhang and Urvesh Bhowan and Bunna Ny",
  title =        "Genetic Programming for Object Detection: A Two-Phase
                 Approach with an Improved Fitness Function",
  journal =      "Electronic Letters on Computer Vision and Image
                 Analysis",
  year =         "2006",
  volume =       "6",
  number =       "1",
  pages =        "27--43",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 Intelligence approaches to computer vis, Image
                 analysis, neural networks",
  ISSN =         "1577-5097",
  URL =          "http://elcvia.cvc.uab.es/public/articles/0601/a2006030-2-art.pdf",
  size =         "17 pages",
  abstract =     "This paper describes two innovations that improve the
                 efficiency and effectiveness of a genetic programming
                 approach to object detection problems. The approach
                 uses genetic programming to construct object detection
                 programs that are applied, in a moving window fashion,
                 to the large images to locate the objects of interest.
                 The first innovation is to break the GP search into two
                 phases with the first phase applied to a selected
                 subset of the training data, and a simplified fitness
                 function. The second phase is initialised with the
                 programs from the first phase, and uses the full set of
                 training data with a complete fitness function to
                 construct the final detection programs. The second
                 innovation is to add a program size component to the
                 fitness function. This approach is examined and
                 compared with a neural network approach on three object
                 detection problems of increasing difficulty. The
                 results suggest that the innovations increase both the
                 effectiveness and the efficiency of the genetic
                 programming search, and also that the genetic
                 programming approach outperforms a neural network
                 approach for the most difficult data set in terms of
                 the object detection accuracy",
}

Genetic Programming entries for Mengjie Zhang Urvesh Bhowan Bunna Ny

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