A Ranking-based Cascade Approach for Unbalanced Data

In this paper we present a cascade-based framework for object detection in which the node classifiers are trained by a learning algorithm based on ranking in- stead of classification error. Such an approach is par- ticularly suited for facing the asymmetry between pos- itive and negative class, that is a huge problem in ob- ject detection applications. Other methods focused on this problem and previously proposed, such as Asym- Boost, rely on an asymmetric weight updating mech- anism of the samples based on a parameter k which estimates the degree of skewing between the classes. Actually such parameter is difficult to choose and re- quires a significant tuning activity during the training phase. On the contrary, our approach is nonparametric and has demonstrated to provide slightly better perfor- mance when compared with AsymBoost on a real detec- tion problem.