A comparison of genetic programming feature extraction languages for image classification

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

  author =       "M. Maghoumi and B. J. Ross",
  booktitle =    "IEEE Symposium on Computational Intelligence for
                 Multimedia, Signal and Vision Processing (CIMSIVP
  title =        "A comparison of genetic programming feature extraction
                 languages for image classification",
  year =         "2014",
  month =        dec,
  abstract =     "Visual pattern recognition and classification is a
                 challenging computer vision problem. Genetic
                 programming has been applied towards automatic visual
                 pattern recognition. One of the main factors in
                 evolving effective classifiers is the suitability of
                 the GP language for defining expressions for feature
                 extraction and classification. This research presents a
                 comparative study of a variety of GP languages suitable
                 for classification. Four different languages are
                 examined, which use different selections of image
                 processing operators. One of the languages does block
                 classification, which means that an image is classified
                 as a whole by examining many blocks of pixels within
                 it. The other languages are pixel classifiers, which
                 determine classification for a single pixel. Pixel
                 classifiers are more common in the GP-vision
                 literature. We tested the languages on different
                 instances of Brodatz textures, as well as aerial and
                 camera images. Our results show that the most effective
                 languages are pixel-based ones with spatial operators.
                 However, as is to be expected, the nature of the image
                 will determine the effectiveness of the language
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIMSIVP.2014.7013278",
  notes =        "Also known as \cite{7013278}",

Genetic Programming entries for M Maghoumi Brian J Ross