Localisation Fitness in GP for Object Detection

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

  author =       "Mengjie Zhang and Malcolm Lett",
  title =        "Localisation Fitness in {GP} for Object Detection",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}",
  year =         "2006",
  month =        "10-12 " # apr,
  editor =       "Franz Rothlauf and Jurgen Branke and 
                 Stefano Cagnoni and Ernesto Costa and Carlos Cotta and 
                 Rolf Drechsler and Evelyne Lutton and Penousal Machado and 
                 Jason H. Moore and Juan Romero and George D. Smith and 
                 Giovanni Squillero and Hideyuki Takagi",
  series =       "LNCS",
  volume =       "3907",
  publisher =    "Springer Verlag",
  address =      "Budapest",
  publisher_address = "Berlin",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-33237-5",
  pages =        "472--483",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3907&spage=472",
  abstract =     "two new fitness functions in genetic programming for
                 object detection particularly object localisation
                 problems. Both fitness functions use weighted F-measure
                 of a genetic program and consider the localisation
                 fitness values of the detected object locations, which
                 are the relative weights of these locations to the
                 target object centres. The first fitness function
                 calculates the weighted localisation fitness of each
                 detected object, then uses these localisation fitness
                 values of all the detected objects to construct the
                 final fitness of a genetic program. The second fitness
                 function calculates the average locations of all the
                 detected object centres then calculates the weighted
                 localisation fitness value of the averaged position.
                 The two fitness functions are examined and compared
                 with an existing fitness function on three object
                 detection problems of increasing difficulty. The
                 results suggest that almost all the objects of interest
                 in the large images can be successfully detected by all
                 the three fitness functions, but the two new fitness
                 functions can result in far fewer false alarms and
                 spend much less training time.",
  notes =        "part of \cite{evows06}",

Genetic Programming entries for Mengjie Zhang Malcolm Lett