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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jones M., Viola P.: Face Recognition Using Boosted Local Features, Mitsubishi Electric Research Laboratories, TR20003-25 (April 2003)
Freund, Y., Schapire, R.E.: A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and Systems Sciences 55(1), 119–139 (1997)
Shapire, R.E.: The Boosting Approach to Machine Learning – An Overview. In: MSRI Workshop on Nonlinear Estimation and Classification (2002)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. Computer Vision and Pattern Recognition (2001)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Skarbek, W., Kucharski, K. (2004). Image Object Localization by AdaBoost Classifier. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-30125-7_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
Online ISBN: 978-3-540-30125-7
eBook Packages: Springer Book Archive