CLASSIFIER ENSEMBLE BASED-ON BI-CODED CHROMOSOME GENETIC ALGORITHM FOR AUTOMATIC IMAGE ANNOTATION

摘要：

Using image classification approach for automatic image annotation is one promising method. In order to improve image annotation accuracy, recent researchers propose to use AdaBoost algorithm for the ensemble of classifiers. But in these researches, only fewer features are used. We construct multi-class classifiers for all the image low-level feature of Multimedia Content Description Interface and their potential combinations respectively, it-nearest neighbor classifier is used as the base classifier and one vs. one scheme is chosen to build multi-class classifiers. A bi-coded chromosome genetic algorithm is used to select the optimal classifier subset as weak classifiers and corresponding weights, which are used for the combination of an ensemble classifier by weighted majority voting scheme. The results of experiment over 2000 classified Corel images show that the approach selects 4 of 325 multi-class classifiers as weak classifiers as well as corresponding optimized weights to generate an ensemble classifier. The ensemble classifier created by the bi-coded chromosome genetic algorithm has higher accuracy than that by AdaBoost algorithm.

关键词： Classifier ensemble Automatic image annotation Bicoded chromosome genetic algorithm K-nearest neighbor classifier Multimedia content description interface

作者: TIAN-ZHONG ZHAO YAN-HUI LI JIAN-JIANG LU YA-FEI ZHANG

作者单位: Institute of Command Automation, PLA University of Science and Technology, Nanjing 210007, China Zhe School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 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 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 22-27

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）