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Image Object Localization by AdaBoost Classifier

  • Władysław Skarbek
  • Krzysztof Kucharski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

AdaBoost as a methodology of aggregation of many weak classifiers into one strong classifier is used now in object detection in images. In particular it appears very efficient in face detection and eye localization. In order to improve the speed of the classifier we show a new scheme for the decision cost evaluation. The aggregation scheme reduces the number of weak classifiers and provides better performance in terms of false acceptance and false rejection ratios.

Keywords

Face Detection Weak Classifier Exponential Convergence False Acceptance Rate False Rejection Rate 
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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References

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    Shapire, R.E.: The Boosting Approach to Machine Learning – An Overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2002)Google Scholar
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    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. Computer Vision and Pattern Recognition (2001)Google Scholar
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    Xiao, R., Li, M.-J., Zhang, H.-J.: Robust Multipose Face Detection in Images. IEEE Trans. on Circuits and Systems for Video Technology (January 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Władysław Skarbek
    • 1
  • Krzysztof Kucharski
    • 1
  1. 1.Warsaw University of Technology 

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