Classifier Ensemble Based-on AdaBoost and Genetic Algorithm for Automatic Image Annotation

摘要：

Image classification approach is one promising method used for automatic image annotation.In order to improve image annotation accuracy,recent researchers propose to use AdaBoost algorithm for the ensemble of classifiers.But as it is difficult for AdaBoost algorithm to search a large feature space, only fewer features are used for the construction of weak classifiers in ensemble.As a result,it is easy to fall into local optimal.We use all the 25 image low-level features of Multimedia Content Description Interface to descript images.Genetic algorithm is used to decrease the search space by randomly select a subset of features.We construct a multi-class weak classifier for each of the features in the subset and their potential combinations respectively.k-nearest neighbor classifier is used as the base classifier and one vs.one scheme is chosen to build multi-class classifiers.Lastly,we use AdaBoost.M1 algorithm to generate an ensemble classifier and optimize it combining with genetic algorithm.The results of experiment over 2000 classified Corel images show that the ensemble classifier generated in larger search space has higher annotation accuracy.

关键词： Automatic image annotation Classifier ensemble Genetic algorithm Multimedia content description interface.

作者: Tianzhong Zhao Jianjiang Lu Yafei Zhang Qi Xiao Weiguang Xu

作者单位: Institute of Command Automation,PLA University of Science and Technology,Nanjing 210007,China;Zhenji Institute of Command Automation,PLA University of Science and Technology,Nanjing 210007,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 1469-1473

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）