Abstract
This paper describes an approach to the refinement of a fitness function and the optimisation of training data in genetic programming for object detection particularly object localisation problems. The approach is examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that the new fitness function outperforms the old one by producing far fewer false alarms and spending much less training time and that some particular types of training examples contain most of the useful information for object detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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, San Francisco (1998)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Song, A., Ciesielski, V., Williams, H.: Texture classifiers generated by genetic programming. In: Fogel, D.B., El-Sharkawi, M.A., Yao, X., Greenwood, G., Iba, H., Marrow, P., Shackleton, M. (eds.) Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, pp. 243–248. IEEE Press, Los Alamitos (2002)
Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, ICGA 1993, University of Illinois at Urbana-Champaign, pp. 303–309. Morgan Kaufmann, San Francisco (1993)
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)
Zhang, M., Ciesielski, V., Andreae, P.: A domain independent window-approach to multiclass object detection using genetic programming. EURASIP Journal on Signal Processing, Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis (8), 841–859 (2003)
Smart, W., Zhang, M.: Classification strategies for image classification in genetic programming. In: Bailey, D. (ed.) Proceeding of Image and Vision Computing Conference, Palmerston North, New Zealand, pp. 402–407 (2003)
Howard, D., Roberts, S.C., Brankin, R.: Target detection in SAR imagery by genetic programming. Advances in Engineering Software 30, 303–311 (1999)
Bhowan, U.: A domain independent approach to multi-class object detection using genetic programming. Master’s thesis, BSc Honours research project/thesis, School of Mathematical and Computing Sciences, Victoria University of Wellington (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, M., Lett, M., Ma, Y. (2006). Refining Fitness Functions and Optimising Training Data in GP for Object Detection. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_76
Download citation
DOI: https://doi.org/10.1007/11903697_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
eBook Packages: Computer ScienceComputer Science (R0)