Localisation Fitness in GP for Object Detection

  • Mengjie Zhang
  • Malcolm Lett
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)


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.


False Alarm Genetic Programming Target Object Object Detection Weighted Localisation 
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  1. 1.
    Gader, P.D., Miramonti, J.R., Won, Y., Coffield, P.: Segmentation free shared weight neural networks for automatic vehicle detection. Neural Networks 8, 1457–1473 (1995)CrossRefGoogle Scholar
  2. 2.
    Roitblat, H.L., Au, W.W.L., Nachtigall, P.E., Shizumura, R., Moons, G.: Sonar recognition of targets embedded in sediment. Neural Networks 8, 1263–1273 (1995)CrossRefGoogle Scholar
  3. 3.
    Roth, M.W.: Survey of neural network technology for automatic target recognition. IEEE Transactions on neural networks 1, 28–43 (1990)CrossRefGoogle Scholar
  4. 4.
    Waxman, A.M., Seibert, M.C., Gove, A., Fay, D.A., Bernandon, A.M., Lazott, C., Steele, W.R., Cunningham, R.K.: Neural processing of targets in visible, multispectral ir and sar imagery. Neural Networks 8, 1029–1051 (1995)CrossRefGoogle Scholar
  5. 5.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction on the Automatic Evolution of computer programs and its Applications. Morgan Kaufmann Publishers, Heidelburg (1998)MATHGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, London (1992)MATHGoogle Scholar
  7. 7.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, London (1994)MATHGoogle Scholar
  8. 8.
    Song, A., Ciesielski, V., Williams, H.: Texture classifiers generated by genetic programming. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 243–248. IEEE Press, Los Alamitos (2002)Google Scholar
  9. 9.
    Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 303–309. Morgan Kaufmann, San Francisco (1993)Google Scholar
  10. 10.
    Zhang, M., Andreae, P., Pritchard, M.: Pixel statistics and false alarm area in genetic programming for object detection. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 455–466. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Zhang, M., Ciesielski, V., Andreae, P.: A domain independent window-approach to multiclass object detection using genetic programming. EURASIP Journal on Signal Processing, 841–859 (2003)Google Scholar
  12. 12.
    Smart, W., Zhang, M.: Classification strategies for image classification in genetic programming. In: Proceeding of Image and Vision Computing Conference, Palmerston North, New Zealand, 402–407 (2003)Google Scholar
  13. 13.
    Howard, D., Roberts, S.C., Brankin, R.: Target detection in SAR imagery by genetic programming. Advances in Engineering Software 30, 303–311 (1999)CrossRefGoogle Scholar
  14. 14.
    Bhowan, U.: A domain independent approach to multi-class object detection using genetic programming. BSc Honours research project, School of Mathematical and Computing Sciences, Victoria University of Wellington (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mengjie Zhang
    • 1
  • Malcolm Lett
    • 1
  1. 1.School of Mathematics, Statistics and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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