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International Journal of Computer Vision

, Volume 103, Issue 3, pp 326–347 | Cite as

Training Effective Node Classifiers for Cascade Classification

  • Chunhua ShenEmail author
  • Peng Wang
  • Sakrapee Paisitkriangkrai
  • Anton van den Hengel
Article

Abstract

Cascade classifiers are widely used in real-time object detection. Different from conventional classifiers that are designed for a low overall classification error rate, a classifier in each node of the cascade is required to achieve an extremely high detection rate and moderate false positive rate. Although there are a few reported methods addressing this requirement in the context of object detection, there is no principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such an algorithm here. We show that a special case of the biased minimax probability machine has the same formulation as the linear asymmetric classifier (LAC) of Wu et al. (linear asymmetric classifier for cascade detectors, 2005). We then design a new boosting algorithm that directly optimizes the cost function of LAC. The resulting totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on object detection verify the effectiveness of the proposed boosting algorithm as a node classifier in cascade object detection, and show performance better than that of the current state-of-the-art.

Keywords

AdaBoost Minimax probability machine Cascade classifier Object detection Human detection 

Notes

Acknowledgments

This work was in part supported by Australian Research Council Future Fellowship FT120100969

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Chunhua Shen
    • 1
    Email author
  • Peng Wang
    • 1
  • Sakrapee Paisitkriangkrai
    • 1
  • Anton van den Hengel
    • 1
  1. 1.Australian Centre for Visual Technologies, School of Computer ScienceThe University of AdelaideNorth Terrace, AdelaideAustralia

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