Genetic Programming for Multiple Class Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Mengjie Zhang and Victor Ciesielski",
  title =        "Genetic Programming for Multiple Class Object
  booktitle =    "12th Australian Joint Conference on Artificial
  year =         "1999",
  editor =       "Norman Foo",
  volume =       "1747",
  series =       "LNAI",
  pages =        "180--192",
  address =      "Sydney, Australia",
  publisher_address = "Berlin",
  month =        "6-10 " # dec,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, Neural networks, Vision",
  ISBN =         "3-540-66822-5",
  URL =          "",
  size =         "13 pages",
  abstract =     "We describe an approach to the use of genetic
                 programming for object detection problems in which the
                 locations of small objects of multiple classes in large
                 pictures must be found. The evolved programs use a
                 feature set computed from a square input field large
                 enough to contain each of objects of interest and are
                 applied, in moving window fashion, over the large
                 pictures in order to locate the objects of interest.
                 The fitness function is based on the detection rate and
                 the false alarm rate. We have tested the method on
                 three object detection problems of increasing
                 difficulty with four different classes of interest. On
                 pictures of easy and medium difficulty all objects are
                 detected with no false alarms. On difficult pictures
                 there are still significant numbers of errors, however
                 the results are considerably better than those of a
                 neural network based program for the same problems.",
  notes =        "",

Genetic Programming entries for Mengjie Zhang Victor Ciesielski