一种融合特征选择的AdaBoost集成算法
|
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 Ada Boost algorithm,a lot of redundant features existed in the training of the classifier. In allusion to the problem mentioned above,an Ada Boost algorithm with integration of feature selection was proposed. Firstly,a feature selection method was developed for our model. Those visual features with small correlations between them was chosen to establish the optimal training set. Secondly,the Ada Boost 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 automatic 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.
In Ada Boost algorithm,a lot of redundant features existed in the training of the classifier. In allusion to the problem mentioned above,an Ada Boost algorithm with integration of feature selection was proposed. Firstly,a feature selection method was developed for our model. Those visual features with small correlations between them was chosen to establish the optimal training set. Secondly,the Ada Boost 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 automatic 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.
引文
[1]HUISKES M J,THOMEE B,LEW M S.New trends and ideas in visual concept detection[C]//Proc of the 11th ACM Conference on Multimedia Information Retrieval,2010:527-536.
[2]LI Zhixin,SHI Zhiping,LI Zhiqing,et al.Automatic image annotation by fusing semantic ropics[J].Journal of Software,2011,22(4):801-812.
[3]吴伟,聂建云,高光来.一种基于改进的支持向量机多分类器图像标注方法[J].计算机工程与科学,2015,37(7):1338-1343.WU Wei,NIE Jianyun,GAO Guanglai.Improved SVM multiple classifiers for image annotation[J].Computer Engineering and Science,2015,37(7):1338-1343.
[4]XU Hongtao,ZHOU Xiangdong.Adaptive model for web image semantic automatic annotation[J].Journal of Software,2010,21(9):2183-2195.
[5]LIU Zheng,MA Jun.Refining image annotation by graph partition and image search engine[J].Journal of Computer Research and Development,2011,48(7):1246-1254.
[6]王小平,李柳柏.基于Ada Boost算法的图像自动标注[J].西南大学学报(自然科学版),2015,37(7):174-180.WANG Xiaoping,LI Liubai.Automatic image annotation based on Ada Boost algorithm[J].Journal of Southwest University(Natural Science),2015,37(7):174-180.
[7]曹莹,苗启广,刘家辰,等.Ada Boost算法研究进展与展望[J].自动化学报,2013,39(6):745-758.CAO Ying,MIAO Qiguang,LIU Jiachen,et al.Advance and prospects of Ada Boost algorithm[J].Acta Automatica Sinica,2013,39(6):745-758.
[8]降爱莲,杨兴彤.基于Ada Boost-SVM级联分类器的行人检测[J].计算机工程与设计,2013,34(7):2547-2550,2565.JIANG Ailian,YANG Xingtong.Pedestrian detection based on cascade Ada Boost-SVM[J].Computer Engineering and Design,2013,34(7):2547-2550,2565.
[2]LI Zhixin,SHI Zhiping,LI Zhiqing,et al.Automatic image annotation by fusing semantic ropics[J].Journal of Software,2011,22(4):801-812.
[3]吴伟,聂建云,高光来.一种基于改进的支持向量机多分类器图像标注方法[J].计算机工程与科学,2015,37(7):1338-1343.WU Wei,NIE Jianyun,GAO Guanglai.Improved SVM multiple classifiers for image annotation[J].Computer Engineering and Science,2015,37(7):1338-1343.
[4]XU Hongtao,ZHOU Xiangdong.Adaptive model for web image semantic automatic annotation[J].Journal of Software,2010,21(9):2183-2195.
[5]LIU Zheng,MA Jun.Refining image annotation by graph partition and image search engine[J].Journal of Computer Research and Development,2011,48(7):1246-1254.
[6]王小平,李柳柏.基于Ada Boost算法的图像自动标注[J].西南大学学报(自然科学版),2015,37(7):174-180.WANG Xiaoping,LI Liubai.Automatic image annotation based on Ada Boost algorithm[J].Journal of Southwest University(Natural Science),2015,37(7):174-180.
[7]曹莹,苗启广,刘家辰,等.Ada Boost算法研究进展与展望[J].自动化学报,2013,39(6):745-758.CAO Ying,MIAO Qiguang,LIU Jiachen,et al.Advance and prospects of Ada Boost algorithm[J].Acta Automatica Sinica,2013,39(6):745-758.
[8]降爱莲,杨兴彤.基于Ada Boost-SVM级联分类器的行人检测[J].计算机工程与设计,2013,34(7):2547-2550,2565.JIANG Ailian,YANG Xingtong.Pedestrian detection based on cascade Ada Boost-SVM[J].Computer Engineering and Design,2013,34(7):2547-2550,2565.