Cascades of boosted classifiers have become increasingly popular in machine vision and have generated a lot of recent research. Most of it has focused on modifying the underlying Adaboost method and far less attention has been given to the problem of dimensioning the cascade, i.e. determining the number and the characteristics of the boosted classifiers. To a large extent, the designer of a cascade must set the parameters in the cascade using ad-hoc methods.

We propose to automatically build a cascade of classifiers, given just a family of weak classifiers a desired performance level and little more. First, a boosted classifier with the desired performance is built using any boosting method. This classifier is then “sliced” using dynamic programming into a cascade of classifiers in a nearly computation-cost-optimal fashion.


Face Detection Validation Dataset Weak Classifier Computational Learn Theory Optimal Cascade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Etienne Grossmann
    • 1
  1. 1.Center for Visualization and Virtual EnvironmentsUniversity of Kentucky 

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