Soft Computing

, Volume 15, Issue 9, pp 1793–1805 | Cite as

Particle swarm optimisation based AdaBoost for object detection

  • Ammar Mohemmed
  • Mark Johnston
  • Mengjie ZhangEmail author


This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only, and the weak classifiers use a simple decision stump. The second uses PSO for both selecting good features and evolving weak classifiers in parallel. These two methods are examined and compared on two challenging object detection tasks in images: detection of individual pasta pieces and detection of a face. The experimental results suggest that both approaches can successfully detect object positions and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only. We also show that PSO can evolve and select meaningful features in the face detection task.


Particle swarm optimisation AdaBoost Object classification Object recognition Face detection Feature selection 



We would like to thank the anonymous referees for their time, comments, and suggestions that provided great help for improving the paper. This work was supported in part by the University Research Fund under the number of URF09-2399/85808 at Victoria University of Wellington for 2008/2009, and the Marsden Fund council from the government funding (08-VUW-014), administrated by the Royal Society of New Zealand.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand
  2. 2.School of Mathematics, Statistics and Operations ResearchVictoria University of WellingtonWellingtonNew Zealand

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