A Gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection

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

@PhdThesis{PeiFang_Guo:thesis,
  author =       "Pei Fang Guo",
  title =        "A Gaussian mixture-based approach to synthesizing
                 nonlinear feature functions for automated object
                 detection",
  school =       "Electrical and Computer Engineering, Concordia
                 University",
  year =         "2010",
  address =      "Canada",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://phdtree.org/pdf/25841242-a-gaussian-mixture-based-approach-to-synthesizing-nonlinear-feature-functions-for-automated-object-detection/",
  URL =          "http://spectrum.library.concordia.ca/979537/1/NR67351.pdf",
  size =         "92 pages",
  abstract =     "Feature design is an important part to identify
                 objects of interest into a known number of categories
                 or classes in object detection. Based on the
                 depth-first search for higher order feature functions,
                 the technique of automated feature synthesis is
                 generally considered to be a process of creating more
                 effective features from raw feature data during the run
                 of the algorithms. This dynamic synthesis of nonlinear
                 feature functions is a challenging problem in object
                 detection. This thesis presents a combinatorial
                 approach of genetic programming and the expectation
                 maximization algorithm (GP-EM) to synthesize nonlinear
                 feature functions automatically in order to solve the
                 given tasks of object detection. The EM algorithm
                 investigates the use of Gaussian mixture which is able
                 to model the behaviour of the training samples during
                 an optimal GP search strategy. Based on the Gaussian
                 probability assumption, the GP-EM method is capable of
                 performing simultaneously dynamic feature synthesis and
                 model-based generalization. The EM part of the approach
                 leads to the application of the maximum likelihood (ML)
                 operation that provides protection against
                 inter-cluster data separation and thus exhibits
                 improved convergence. Additionally, with the GP-EM
                 method, an innovative technique, called the histogram
                 region of interest by thresholds (HROIBT), is
                 introduced for diagnosing protein conformation defects
                 (PCD) from microscopic imagery. The experimental
                 results show that the proposed approach improves the
                 detection accuracy and efficiency of pattern object
                 discovery, as compared to single GP-based feature
                 synthesis methods and also a number of other object
                 detection systems. The GP-EM method projects the
                 hyperspace of the raw data onto lower-dimensional
                 spaces efficiently, resulting in faster computational
                 classification processes.",
  notes =        "Supervisors: Prabir Bhattacharya and Nawwaf Kharma

                 ID Code: 979537",
}

Genetic Programming entries for Pei Fang Guo

Citations