摘要
针对传统AdaBoost算法的不足,分析了分类器训练时间长和训练过程中容易出现训练结果对训练样本严重收敛的问题(过训练),并提出了解决这一问题的有效方法.新方法主要将特征值和排序结果进行缓存以及对训练样本及时更新.使用该方法训练级联车牌检测器,实验结果表明,新方法较好地解决了传统AdaBoost算法中所出现的过训练和训练耗时的问题,在提高检测率的同时降低了误检率,并且训练时间缩短了50%左右.
Focusing on the disadvantages of classical AdaBoost algorithm,this paper mainly analyzes the issue that the training time for classifiers is time-consuming and in training process the training results severely constringe the training samples（excessive training）and a new method is advanced to avoid the problems. The new method is to buffer the computational results of sorted feature values and update the training samples in time. As a result,using the method to train a cascade license plate,the experimental results show that the new method does not lead to the issue of excessive training and time consuming like classical AdaBoost often does,and moreover,the training time is shortened to 50 percent with a high detection rate and a low false alarm rate.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第7期40-43,48,共5页
Microelectronics & Computer
基金
重庆市教委资助项目(KJ090519)
