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Localisation Fitness in GP for Object Detection

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Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

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Abstract

This paper describes two new fitness functions in genetic programming for object detection particularly object localisation problems. Both fitness functions use weighted F-measure of a genetic program and consider the localisation fitness values of the detected object locations, which are the relative weights of these locations to the target object centers. The first fitness function calculates the weighted localisation fitness of each detected object, then uses these localisation fitness values of all the detected objects to construct the final fitness of a genetic program. The second fitness function calculates the average locations of all the detected object centres then calculates the weighted localisation fitness value of the averaged position. The two fitness functions are examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that almost all the objects of interest in the large images can be successfully detected by all the three fitness functions, but the two new fitness functions can result in far fewer false alarms and spend much less training time.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand

    Mengjie Zhang & Malcolm Lett

Authors
  1. Mengjie Zhang
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  2. Malcolm Lett
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhang, M., Lett, M. (2006). Localisation Fitness in GP for Object Detection. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_42

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  • DOI: https://doi.org/10.1007/11732242_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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