Networks of transform-based evolvable features for object recognition
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Kowaliw:2013:GECCO,
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author = "Taras Kowaliw and Wolfgang Banzhaf and Rene Doursat",
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title = "Networks of transform-based evolvable features for
object recognition",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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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",
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isbn13 = "978-1-4503-1963-8",
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pages = "1077--1084",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463507",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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
Citations