Genetic Programming for Edge Detection via Balancing Individual Training Images

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  title =        "Genetic Programming for Edge Detection via Balancing
                 Individual Training Images",
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  pages =        "2702--2709",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6252879",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Conflict of
                 Interest Papers, Classification, clustering, data
                 analysis and data mining",
  abstract =     "Edge detectors trained by a machine learning algorithm
                 are usually evaluated by the accuracy based on overall
                 pixels in the training stage, rather than the
                 information for each training image. However, when the
                 evaluation for training edge detectors considers the
                 accuracy of each image, the influence on the final
                 detectors has not been investigated. In this study, we
                 employ genetic programming to evolve detectors with new
                 fitness functions containing the accuracy of training
                 images. The experimental results show that fitness
                 functions based on the accuracy of single training
                 images can balance the accuracies across detection
                 results, and the fitness function combining the
                 accuracy of overall pixels with the accuracy of
                 training images together can improve the detection
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang