Response Binning: Improved Weak Classifiers for Boosting

Abstract

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