Evaluations of Feature Extraction Programs Synthesized by Redundancy-removed Linear Genetic Programming: A Case Study on the Lawn Weed Detection Problem

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@Article{journals/jip/WatchareeruetaiTMKO10,
  author =       "Ukrit Watchareeruetai and Yoshinori Takeuchi and 
                 Tetsuya Matsumoto and Hiroaki Kudo and Noboru Ohnishi",
  title =        "Evaluations of Feature Extraction Programs Synthesized
                 by Redundancy-removed Linear Genetic Programming: A
                 Case Study on the Lawn Weed Detection Problem",
  journal =      "Journal of Information Processing",
  year =         "2010",
  volume =       "18",
  pages =        "164--174",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.2197/ipsjjip.18.164",
  size =         "11 pages",
  abstract =     "This paper presents an evolutionary synthesis of
                 feature extraction programs for object recognition. The
                 evolutionary synthesis method employed is based on
                 linear genetic programming which is combined with
                 redundancy-removed recombination. The evolutionary
                 synthesis can automatically construct feature
                 extraction programs for a given object recognition
                 problem, without any domain-specific knowledge.
                 Experiments were done on a lawn weed detection problem
                 with both a low-level performance measure, i.e.,
                 segmentation accuracy, and an application-level
                 performance measure, i.e., simulated weed control
                 performance. Compared with four human-designed lawn
                 weed detection methods, the results show that the
                 performance of synthesised feature extraction programs
                 is significantly better than three human-designed
                 methods when evaluated with the low-level measure, and
                 is better than two human-designed methods according to
                 the application-level measure.",
  notes =        "Department of Media Science, Graduate School of
                 Information Science, Nagoya University",
  bibdate =      "2011-09-14",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/jip/jip18.html#WatchareeruetaiTMKO10",
}

Genetic Programming entries for Ukrit WatchAreeruetai Yoshinori Takeuchi Tetsuya Matsumoto Hiroaki Kudo Noboru Ohnishi

Citations