Response Binning: Improved Weak Classifiers for Boosting

This paper demonstrates the value of improving the discriminating strength of weak classifiers in the context of boosting by using response binning. The reasoning is centered around, but not limited to, the well known Haar-features used by Viola and Jones (2001) in their face detection/pedestrian de...

Full description

Saved in:
Bibliographic Details
Published in2006 IEEE Intelligent Vehicles Symposium pp. 344 - 349
Main Authors Rasolzadeh, B., Petersson, L., Pettersson, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Algorithm design and analysis
Australia
Automobiles
Boosting
Computer vision
Face detection
Filters
Intelligent vehicles
Pattern recognition
Performance analysis
Online AccessGet full text
ISBN490112286X
9784901122863
ISSN1931-0587
DOI10.1109/IVS.2006.1689652

Cover

More Information
Summary:This paper demonstrates the value of improving the discriminating strength of weak classifiers in the context of boosting by using response binning. The reasoning is centered around, but not limited to, the well known Haar-features used by Viola and Jones (2001) in their face detection/pedestrian detection systems. It is shown that using a weak classifier based on a single threshold is sub-optimal and in the case of the Haar-feature inadequate. A more general method for features with multi-modal responses is derived that is easily used in boosting mechanisms that accepts a confidence measure, such as the RealBoost algorithm. The method is evaluated by boosting a single stage classifier and compare the performance to previous approaches
ISBN:490112286X
9784901122863
ISSN:1931-0587
DOI:10.1109/IVS.2006.1689652