Evolutionary learning of local descriptor operators for object recognition

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

@InProceedings{DBLP:conf/gecco/PerezO09,
  author =       "Cynthia B. Perez and Gustavo Olague",
  title =        "Evolutionary learning of local descriptor operators
                 for object recognition",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1051--1058",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570043",
  abstract =     "Nowadays, object recognition is widely studied under
                 the paradigm of matching local features. This work
                 describes a genetic programming methodology that
                 synthesizes mathematical expressions that are used to
                 improve a well known local descriptor algorithm. It
                 follows the idea that object recognition in the
                 cerebral cortex of primates makes use of features of
                 intermediate complexity that are largely invariant to
                 change in scale, location, and illumination. These
                 local features have been previously designed by human
                 experts using traditional representations that have a
                 clear, preferably mathematically, well-founded
                 definition. However, it is not clear that these same
                 representations are implemented by the natural system
                 with the same structure. Hence, the possibility to
                 design novel operators through genetic programming
                 represents an open research avenue where the
                 combinatorial search of evolutionary algorithms can
                 largely exceed the ability of human experts. This paper
                 provides evidence that genetic programming is able to
                 design new features that enhance the overall
                 performance of the best available local descriptor.
                 Experimental results confirm the validity of the
                 proposed approach using a widely accept testbed and an
                 object recognition application.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Cynthia B Perez Gustavo Olague

Citations