Objective Classification Using Advanced Adaboost Algorithm
Kunhui Lin, Ruohe Yan, Hong Duan, Junfeng Yao, Changle Zhou · 2008
Adaboost, a general method for improving the accuracy of any given learning algorithm, is usually used to solve the problem of object detection based on cascade structure. However it has some disadvantage. The paper proposes an advanced Adaboost algorithm for object detection. The algorithm adopts a new method to update weighted parameters of weak classifiers. The weights are affected not only by the error rates, but also by their capacity of positive recognition. It is more adaptive to the object detection by decreasing the false alarm rates in the low false rejection rate terminal. The experiment results show the improvement achieved by the new algorithm.