An efficient image pattern recognition system using an evolutionary search strategy

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Pei-Fang Guo and Prabir Bhattacharya and 
                 Nawwaf Kharma",
  title =        "An efficient image pattern recognition system using an
                 evolutionary search strategy",
  booktitle =    "IEEE International Conference on Systems, Man and
                 Cybernetics, SMC 2009",
  year =         "2009",
  month =        oct,
  pages =        "599--604",
  address =      "San Antonio, Texas, USA",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, EM, GP,
                 Gaussian mixture estimation, HROIT, OPMD disease
                 diagnosis, efficiency 90.20 percent, evolutionary
                 genetic programming, evolutionary search strategy,
                 expectation maximization algorithm, feature function
                 generation, histogram region, image pattern recognition
                 system, image thresholding, oculopharyngeal muscular
                 dystrophy, primitive texture feature extraction,
                 support vector machine, Gaussian processes, diseases,
                 expectation-maximisation algorithm, eye, feature
                 extraction, image recognition, image segmentation,
                 image texture, medical image processing, muscle, search
  ISSN =         "1062-922X",
  DOI =          "doi:10.1109/ICSMC.2009.5346614",
  size =         "6 pages",
  abstract =     "A mechanism involving evolutionary genetic programming
                 (GP) and the expectation maximization algorithm (EM) is
                 proposed to generate feature functions automatically,
                 based on the primitive features, for an image pattern
                 recognition system on the diagnosis of the disease
                 OPMD. Prior to the feature function generation, we
                 introduce a novel technique of the primitive texture
                 feature extraction, which deals with non-uniform
                 images, from the histogram region of interest by
                 thresholds (HROIT). Compared with the performance
                 achieved by support vector machine (SVM) using the
                 whole primitive texture features, the GP-EM
                 methodology, as a whole, achieves a better performance
                 of 90.20percent recognition rate on diagnosis, while
                 projecting the hyperspace of the primitive features
                 onto the space of a single generated feature.",
  notes =        "Also known as \cite{5346614}",

Genetic Programming entries for Pei Fang Guo Prabir Bhattacharya Nawwaf Kharma