Classification of Images using Color, CBIR Distance Measures and Genetic Programming: An evolutionary Experiment

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

@Misc{Edvardsen:undergraduatethesis,
  author =       "Stian Edvardsen",
  title =        "Classification of Images using Color, CBIR Distance
                 Measures and Genetic Programming: An evolutionary
                 Experiment",
  howpublished = "Undergraduate Theses from Norwegian University of
                 Science and Technology. Faculty of Information
                 Technology, Mathematics and Electrical Engineering,
                 Department of Computer and Information Science",
  year =         "2006",
  month =        jun,
  type =         "Undergraduate thesis Masteroppgave-level",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://ntnu.diva-portal.org/smash/get/diva2:348194/FULLTEXT01.pdf",
  URL =          "http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9327",
  size =         "151 pages",
  abstract =     "In this thesis a novel approach to image
                 classification is presented. The thesis explores the
                 use of colour feature vectors and CBIR, retrieval
                 methods in combination with Genetic Programming to
                 achieve a classification system able to build classes
                 based on training sets, and determine if an image is a
                 part of a specific class or not.

                 A test bench has been built, with methods for
                 extracting colour features, both segmented and whole,
                 from images. CBIR distance-algorithms have been
                 implemented, and the algorithms used are histogram
                 Euclidian distance, histogram intersection distance and
                 histogram quadratic distance. The genetic program
                 consists of a function set for adjusting weights which
                 corresponds to the extracted feature vectors. Fitness
                 of the individual genomes is measured by using the CBIR
                 distance algorithms, seeking to minimise the distance
                 between the individual images in the training set. A
                 classification routine is proposed, using the feature
                 vectors from the image in question, and weights
                 generated in the genetic program in order to determine
                 if the image belongs to the trained class.

                 A test-set of images is used to determine the accuracy
                 of the method. The results shows that it is possible to
                 classify images using this method, but that it requires
                 further exploration to make it capable of good
                 results.",
  notes =        "Supervisor: Ramampiaro, Herindrasana",
}

Genetic Programming entries for Stian Edvardsen

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