一种融合特征选择的AdaBoost集成算法  

An Ada Boost Algorithm with Integration of Feature Selection

作　　者：陈旭生[1] 苏辉[2] 冯岩[1] 

机构地区：[1]信阳师范学院计算机与信息技术学院,河南信阳464000 [2]信阳师范学院网络信息与计算中心,河南信阳464000

出　　处：《信阳师范学院学报（自然科学版）》2017年第2期304-308,共5页Journal of Xinyang Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61572417);河南省科技计划项目(152102210129);河南省教师教育课程改革研究项目(2017-JSJYYB-055);河南省高等学校重点科研项目(17B520034);信阳师范学院青年基金项目(2014-QN-054;2014-QN-056)

摘　　要：针对Ada Boost算法训练分类器的特征具有大量冗余问题,提出了一种融合特征选择的Ada Boost集成算法.首先,使用一种特征选取方法,选择图像特征之间冗余度最小的特征,构造最优训练集;其次,采用Ada Boost算法训练分类器,构建分类模型;最后,使用分类模型实现待标注图像的自动标注.实验使用华盛顿大学用于图像自动标注的数据集,结果验证算法的有效性,并且相比其他传统算法,该算法具有更高的分类精度.In AdaBoost algorithm, a lot of redundant features existed in the training of the classifier. In allusion to the problem mentioned above, an AdaBoost algorithm with integration of feature selection was proposed. Firstly, a fea- ture selection method was developed for our model. Those visual features with small correlations between them was cho- sen to establish the optimal training set. Secondly, the AdaBoost integrated classification algorithm was used to train the classifier and to establish the classifier model. Finally, the classifier model mentioned above was used to realize the au- tomatic annotation of the unlabeled images. Based the university of Washington image dataset, the experiment results showed that the proposed feature selection method was very suitable for the classifier, and compared with other classical algorithms, the algorithm given in this paper had the optimal accuracy classification.

关 键 词：图像标注 特征选择 ADABOOST算法 分类器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]