Ent-Boost: Boosting Using Entropy Measure for Robust Object Detection

Recently, boosting is used widely in object detection applications because of its impressive performance in both speed and accuracy. However, learning weak classifiers which is one of the most significant tasks in using boosting is left for users. This paper describes a novel method for efficiently learning weak classifiers using entropy measures, called Ent-Boost. The class entropy information is used to estimate the optimal number of bins automatically through discretization process. Then Kullback-Leibler divergence which is the relative entropy between probability distributions of positive and negative samples is employed to select the best weak classifier in the weak classifier set. Experiments have shown that strong classifiers learned by Ent-Boost can achieve good performance, and have compact storage space. Results on building a robust face detector are also reported.