A Multistage Approach To Cooperatively Coevolving Feature Construction and Object Detection

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

@InProceedings{roberts:evows05,
  author =       "Mark E. Roberts and Ela Claridge",
  title =        "A Multistage Approach To Cooperatively Coevolving
                 Feature Construction and Object Detection",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoMUSART}, {EvoSTOC}",
  year =         "2005",
  month =        "30 " # mar # "-1 " # apr,
  editor =       "Franz Rothlauf and Juergen Branke and 
                 Stefano Cagnoni and David W. Corne and Rolf Drechsler and 
                 Yaochu Jin and Penousal Machado and Elena Marchiori and 
                 Juan Romero and George D. Smith and Giovanni Squillero",
  series =       "LNCS",
  volume =       "3449",
  publisher =    "Springer Verlag",
  address =      "Lausanne, Switzerland",
  publisher_address = "Berlin",
  pages =        "396--406",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation",
  ISBN =         "3-540-25396-3",
  ISSN =         "0302-9743",
  DOI =          "doi:10.1007/b106856",
  URL =          "http://www.cs.bham.ac.uk/~mer/papers/evoiasp-2005.pdf",
  abstract =     "In previous work, we showed how cooperative
                 coevolution could be used to evolve both the feature
                 construction stage and the classification stage of an
                 object detection algorithm. Evolving both stages
                 simultaneously allows highly accurate solutions to be
                 created while needing only a fraction of the number of
                 features extracting as in generic approaches.
                 Scalability issues in the previous system have
                 motivated the introduction of a multi-stage approach
                 which has been shown in the literature to provide large
                 reductions in computational requirements. In this work
                 we show how using the idea of coevolutionary feature
                 extraction in conjunction with this multi-stage
                 approach can reduce the computational requirements by
                 at least two orders of magnitude, allowing the
                 impressive performance gains of this technique to be
                 readily applied to many real world problems.",
  notes =        "EvoWorkshops2005",
}

Genetic Programming entries for Mark E Roberts Ela Claridge

Citations