Object detection in multi-modal images using genetic programming

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

@Article{bhanu:2004:ASC,
  author =       "Bir Bhanu and Yingqiang Lin",
  title =        "Object detection in multi-modal images using genetic
                 programming",
  journal =      "Applied Soft Computing",
  year =         "2004",
  volume =       "4",
  number =       "2",
  pages =        "175--201",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.sciencedirect.com/science/article/B6W86-4BV444R-1/2/7540dd938c0b2f3059b1afb5382bd28a",
  DOI =          "doi:10.1016/j.asoc.2004.01.004",
  abstract =     "In this paper, we learn to discover composite
                 operators and features that are synthesized from
                 combinations of primitive image processing operations
                 for object detection. Our approach is based on genetic
                 programming (GP). The motivation for using GP-based
                 learning is that we hope to automate the design of
                 object detection system by automatically synthesizing
                 object detection procedures from primitive operations
                 and primitive features. There are many basic operations
                 that can operate on images and the ways of combining
                 these primitive operations to perform meaningful
                 processing for object detection are almost infinite.
                 The human expert, limited by experience, knowledge and
                 time, can only try a very small number of conventional
                 combinations. Genetic programming, on the other hand,
                 attempts many unconventional combinations that may
                 never be imagined by human experts. In some cases,
                 these unconventional combinations yield exceptionally
                 good results. To improve the efficiency of GP, we
                 propose soft composite operator size limit to control
                 the code-bloat problem while at the same time avoid
                 severe restriction on the GP search. Our experiments,
                 which are performed on selected regions of images to
                 improve training efficiency, show that GP can
                 synthesize effective composite operators consisting of
                 pre-designed primitive operators and primitive features
                 to effectively detect objects in images and the learned
                 composite operators can be applied to the whole
                 training image and other similar testing images.",
}

Genetic Programming entries for Bir Bhanu Yingqiang Lin

Citations