Advertisement

Incorporation of Particle Swarm Optimization in Adaptive Boosting

  • Gaurav Mishra
  • Rohit Kumar
  • Santanu Chaudhury
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

This paper proposes an optimized learning method for large feature-sets using AdaBoost to produce hardware-efficient boosted decision stumps. The paper also proposes a method for training decision stumps to construct the ensemble. AdaBoost sequentially searches for the best weak classifier in the pool and adds it to the ensemble, using weighted training samples. In the proposed method, Particle Swarm Optimization quickens the selection of decision stumps. It is shown experimentally that the optimized method is more than 60% faster than the exhaustive search method.

References

  1. [1]
    Viola, P., Jones, M.: Robust Real-time Object Detection. International Journal of Computer Vision (2001)Google Scholar
  2. [2]
    Zhu, Q., Avidan, S., Yeh, M., Cheng, K.: Fast human detection using a cascade of histograms of oriented gradients. In: CVPR 2006, pp. 1491–1498 (2006)Google Scholar
  3. [3]
    Jia, H., Zhang, Y.: Fast Human Detection by Boosting Histograms of Oriented Gradients. In: Fourth International Conference on Image and Graphics, pp. 683–688 (2007)Google Scholar
  4. [4]
    Miteran, J., Matas, J., Bourennane, E., Paindavoine, M., Dubois, J.: Automatic hardware implementation tool for a discrete adaboost-based decision algorithm. EURASIP Journal on Applied Signal Processing, 1035–1046 (2005)Google Scholar
  5. [5]
    Evans, H., Zhang, M.: Particle swarm optimisation for object classification. In: 23rd International Conference on Image and Vision Computing, New Zealand, pp. 1–6 (2008)Google Scholar
  6. [6]
    Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of on-Line Learning and An Application to Boosting (1995)Google Scholar
  7. [7]
    Kennedy, J., Eberhart, R.: Particle swarm optimization Proceedings. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  8. [8]
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR, pp. 886–893 (2005)Google Scholar
  9. [9]
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. WileyInterscience (2004)Google Scholar
  10. [10]
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 80–91. ACM (1998)Google Scholar
  11. [11]
    Eberhart, R.C., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA (2000)Google Scholar
  12. [12]
    Bergh, F., Engelbrecht, A.P.: A Convergence Proof for the Particle Swarm Optimiser (2010)Google Scholar
  13. [13]
    Guyon, I., Gunn, S.R., Ben-Hur, A., Dror, G.: Result analysis of the NIPS 2003 feature selection challenge (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gaurav Mishra
    • 1
  • Rohit Kumar
    • 1
  • Santanu Chaudhury
    • 1
  1. 1.Electrical Engineering DepartmentIndian Institute of TechnologyIndia

Personalised recommendations