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 instead of classification error. Such an approach is particularly suited for facing the asymmetry between positive and negative class, that is a huge problem in object detection applications. Other methods focused on this problem and previously proposed, such as AsymBoost, rely on an asymmetric weight updating mechanism 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 requires a significant tuning activity during the training phase. On the contrary, our approach is nonparametric and has demonstrated to provide slightly better performance when compared with AsymBoost on a real detection problem.