Neural Networks and Genetic Algorithms for Domain Independent Multiclass Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Mengjie Zhang and Victor Ciesielski",
  title =        "Neural Networks and Genetic Algorithms for Domain
                 Independent Multiclass Object Detection",
  journal =      "International Journal of Computational Intelligence
                 and Applications",
  year =         "2004",
  volume =       "4",
  number =       "1",
  pages =        "77--108",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Network
                 training, network refinement, network sweeping,
                 evolutionary process, domain independent, object
                 recognition, target recognition, target detection",
  ISSN =         "1469-0268",
  DOI =          "doi:10.1142/S146902680400115X",
  abstract =     "This paper describes a domain independent approach to
                 multiple class rotation invariant 2D object detection
                 problems. The approach avoids preprocessing,
                 segmentation and specific feature extraction. Instead,
                 raw image pixel values are used as inputs to the
                 learning systems. Five object detection methods have
                 been developed and tested, the basic method and four
                 variations which are expected to improve the accuracy
                 of the basic method. In the basic method cutouts of the
                 objects of interest are used to train multilayer feed
                 forward networks using back propagation. The trained
                 network is then used as a template to sweep the full
                 image and find the objects of interest. The variations
                 are (1) Use of a centred weight initialisation method
                 in network training, (2) Use of a genetic algorithm to
                 train the network, (3) Use of a genetic algorithm, with
                 fitness based on detection rate and false alarm rate,
                 to refine the weights found in basic approach, and (4)
                 Use of the same genetic algorithm to refine the weights
                 found by method 2. These methods have been tested on
                 three detection problems of increasing difficulty: an
                 easy database of circles and squares, a medium
                 difficulty database of coins and a very difficult
                 database of retinal pathologies. For detecting the
                 objects in all classes of interest in the easy and the
                 medium difficulty problems, a 100percent detection rate
                 with no false alarms was achieved. However the results
                 on the retinal pathologies were unsatisfactory. The
                 centred weight initialization algorithm improved the
                 detection performance over the basic approach on all
                 three databases. In addition, refinement of weights
                 with a genetic algorithm significantly improved
                 detection performance on the three databases.

                 The goal of domain independent object recognition was
                 achieved for the detection of relatively small regular
                 objects in larger images with relatively uncluttered
                 backgrounds. Detection performance on irregular objects
                 in complex, highly cluttered backgrounds such as the
                 retina pictures, however, has not been achieved to an
                 acceptable level.",
  notes =        "",

Genetic Programming entries for Mengjie Zhang Victor Ciesielski