Networks of transform-based evolvable features for object recognition

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

  author =       "Taras Kowaliw and Wolfgang Banzhaf and Rene Doursat",
  title =        "Networks of transform-based evolvable features for
                 object recognition",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1077--1084",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463507",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We propose an evolutionary feature creator (EFC) to
                 explore a non-linear and offline method for generating
                 features in image recognition tasks. Our model aims at
                 extracting low-level features automatically when
                 provided with an arbitrary image database. In this
                 work, we are concerned with the addition of algorithmic
                 depth to a genetic programming (GP) system, suggesting
                 that it will improve the capacity for solving problems
                 that require high-level, hierarchical reasoning. For
                 this we introduce a network superstructure that
                 co-evolves with our low-level GP representations. Two
                 approaches are described: the first uses our previously
                 used 'shallow' GP system, the second presents a new
                 'deep' GP system that involves this network
                 superstructure. We evaluate these models against a
                 benchmark object recognition database. Results show
                 that the deep structure outperforms the shallow one in
                 generating features that support classification, and
                 does so without requiring significant additional
                 computational time. Further, high accuracy is achieved
                 on the standard ETH-80 classification task, also
                 outperforming many existing specialised techniques. We
                 conclude that our EFC is capable of data-driven
                 extraction of useful features from an object
                 recognition database.",
  notes =        "Also known as \cite{2463507} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",

Genetic Programming entries for Taras Kowaliw Wolfgang Banzhaf Rene Doursat