Pixel-wise skin colour detection based on flexible neural tree

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@Article{Xu:2013:ietIP,
  author =       "Tao Xu and Yunhong Wang and Zhaoxiang Zhang",
  journal =      "IET Image Processing",
  title =        "Pixel-wise skin colour detection based on flexible
                 neural tree",
  year =         "2013",
  month =        nov,
  volume =       "7",
  number =       "8",
  pages =        "751--761",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1049/iet-ipr.2012.0657",
  ISSN =         "1751-9659",
  abstract =     "Skin colour detection plays an important role in image
                 processing and computer vision. Selection of a suitable
                 colour space is one key issue. The question that which
                 colour space is most appropriate for pixel-wise skin
                 colour detection is not yet concluded. In this study, a
                 pixel-wise skin colour detection method is proposed
                 based on the flexible neural tree (FNT) without
                 considering the problem of selecting a suitable colour
                 space. A FNT-based skin model is constructed by using
                 large skin data sets which identifies the important
                 components of colour spaces automatically. Experimental
                 results show improved accuracy and false positive rates
                 (FPRs). The structure and parameters of FNT are
                 optimised via genetic programming and particle swarm
                 optimisation algorithms, respectively. In the
                 experiments, nine FNT skin models are constructed and
                 evaluated on features extracted from RGB, YCbCr, HSV
                 and CIE-Lab colour spaces. The Compaq and ECU datasets
                 are used for constructing FNT-based skin model and
                 evaluating its performance compared with other skin
                 detection methods. Without extra processing steps, the
                 authors method achieves state of the art performance in
                 skin pixel classification and better performance in
                 terms of accuracy and FPRs.",
  notes =        "Also known as \cite{6668526}",
}

Genetic Programming entries for Tao Xu Yunhong Wang Zhaoxiang Zhang

Citations