Binary image classification using genetic programming based on local binary patterns

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Al-Sahaf:2013:IVCNZ,
  author =       "Harith Al-Sahaf and Mengjie Zhang and Mark Johnston",
  title =        "Binary image classification using genetic programming
                 based on local binary patterns",
  booktitle =    "28th International Conference of Image and Vision
                 Computing New Zealand (IVCNZ 2013)",
  year =         "2013",
  pages =        "220--225",
  address =      "Wellington",
  month =        nov,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, computer
                 vision, image classification, learning (artificial
                 intelligence), statistical analysis, ANOVA, GP based
                 methods, LBP, SVM, binary image classification,
                 computer vision, image descriptor, learning instances,
                 local binary patterns, machine learning, nonGP methods,
                 one-way analysis of variance, support vector machine,
                 wrapped classifiers, Accuracy, Analysis of variance,
                 Feature extraction, Histograms, Support vector
                 machines, Training, Vectors",
  DOI =          "doi:10.1109/IVCNZ.2013.6727019",
  size =         "6 pages",
  abstract =     "Image classification represents an important task in
                 machine learning and computer vision. To capture
                 features covering a diversity of different objects, it
                 has been observed that a sufficient number of learning
                 instances are required to efficiently estimate the
                 models' parameter values. In this paper, we propose a
                 genetic programming (GP) based method for the problem
                 of binary image classification that uses a single
                 instance per class to evolve a classifier. The method
                 uses local binary patterns (LBP) as an image
                 descriptor, support vector machine (SVM) as a
                 classifier, and a one-way analysis of variance (ANOVA)
                 as an analyser. Furthermore, a multi-objective fitness
                 function is designed to detect distinct and informative
                 regions of the images, and measure the goodness of the
                 wrapped classifiers. The performance of the proposed
                 method has been evaluated on six data sets and compared
                 to the performances of both GP based (Two-tier GP and
                 conventional GP) and non-GP (Naive Bayes, Support
                 Vector Machines and hybrid Naive Bayes/Decision Trees)
                 methods. The results show that a comparable or
                 significantly better performance has been achieved by
                 the proposed method over all methods on all of the data
                 sets considered.",
  notes =        "also known as \cite{6727019}",
}

Genetic Programming entries for Harith Al-Sahaf Mengjie Zhang Mark Johnston

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