Object Detection via Feature Synthesis Using MDL-Based Genetic Programming

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

@Article{bb38973,
  author =       "Yingqiang Lin and Bir Bhanu",
  title =        "Object Detection via Feature Synthesis Using
                 {MDL}-Based Genetic Programming",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics,
                 Part B",
  volume =       "35",
  year =         "2005",
  number =       "3",
  month =        jun,
  pages =        "538--547",
  bibsource =    "http://iris.usc.edu/Vision-Notes/bibliography/pattern650.html#TT36418",
  keywords =     "genetic algorithms, genetic programming, Feature
                 learning, minimum description length (MDL), primitive
                 feature image, primitive operator, synthetic aperture
                 radar (SAR) image",
  ISSN =         "1083-4419",
  URL =          "http://ieeexplore.ieee.org/iel5/3477/30862/01430837.pdf",
  DOI =          "doi:10.1109/TSMCB.2005.846656",
  size =         "10 pages",
  abstract =     "we use genetic programming (GP) to synthesise
                 composite operators and composite features from
                 combinations of primitive operations and primitive
                 features for object detection. The motivation for using
                 GP is to overcome the human experts' limitations of
                 focusing only on conventional combinations of primitive
                 image processing operations in the feature synthesis.
                 GP attempts many unconventional combinations that in
                 some cases yield exceptionally good results. To improve
                 the efficiency of GP and prevent its well-known code
                 bloat problem without imposing severe restriction on
                 the GP search, we design a new fitness function based
                 on minimum description length principle to incorporate
                 both the pixel labelling error and the size of a
                 composite operator into the fitness evaluation process.
                 To further improve the efficiency of GP, smart
                 crossover, smart mutation and a public library ideas
                 are incorporated to identify and keep the effective
                 components of composite operators. Our experiments,
                 which are performed on selected training regions of a
                 training image to reduce the training time, show that
                 compared to normal GP, our GP algorithm finds effective
                 composite operators more quickly and the learned
                 composite operators can be applied to the whole
                 training image and other similar testing images. Also,
                 compared to a traditional region-of-interest extraction
                 algorithm, the composite operators learned by GP are
                 more effective and efficient for object detection.",
}

Genetic Programming entries for Yingqiang Lin Bir Bhanu

Citations