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A Two Phase Genetic Programming Approach to Object Detection

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))

Abstract

This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data to construct the final detection programs. The second innovation is to add a program size component to the fitness function. Application of this approach to three object detection problems indicated that the innovations increased both the effectiveness and the efficiency of the genetic programming search.

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Author information

Authors and Affiliations

  1. School of Mathematical and Computing Sciences, Victoria University of Wellington, P. O. Box 600, Wellington, New Zealand

    Mengjie Zhang, Peter Andreae & Urvesh Bhowan

Authors
  1. Mengjie Zhang
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  2. Peter Andreae
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  3. Urvesh Bhowan
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhang, M., Andreae, P., Bhowan, U. (2004). A Two Phase Genetic Programming Approach to Object Detection. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_32

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  • DOI: https://doi.org/10.1007/978-3-540-30134-9_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

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