Adaboost detector based on multiple thresholds for weak classifier
Jie Qi Ling · Computer Engineering and Applications Journal · 2009
Recently the human face detection system based on Adaboost is successfully used in application areas because of its high speed and accepted detection rates.However,the Adaboost algorithms using the single threshold weak classifiers are too weak to fit complex distributions,and the training procedure is hard to converge.To overcome this dilemma,this paper provides classification trees as a weak learner.The learner greedily splits the node which causes the biggest reduction in measure of error as the partition criteria and builds a weak classifier.Then boosts a weak classifier using real Adaboost or gentle Adaboost methods on training dataset given in data and labels.Experimental results show that using multiple thresholds for the weak classifier can improve the performance of the classifier significantly.