北京交通大学学报2017，Vol.41Issue(6)：21-26,6.DOI:10.11860/j.issn.1673-0291.2017.06.004
深度模型集成的不良图像分类
Illegal image classification based on ensemble deep model
摘要
Abstract
The rapid development of mobile communication technology has greatly promoted the communication experience of users.How to identify and filter out the illegal content in a large a-mount of data is crucial for improving the ability and level of illegal information management in China Mobile.Towards this end,this paper proposes an ensemble deep model (EDM)to classify illegal images.In this approach,several deep models with diverse network structures and comple-mentary information are integrated by using the proposed scheme,and the illegal images with di-verse distributions will be distinguished.To evaluate the effectiveness of the proposed approach, we first collect and set up an illegal image dataset,and compare the proposed approach with the traditional support vector machine(SVM)based image classification method and Alexnet-based, VGG-based and Googlenet-based methods.Experiments show that the proposed approach clearly outperforms the existing methods and obtains excellent classification performance in accurate (94％),precision (84％)and recall (98％).关键词
图像分类/不良信息检测/深度学习/SVM分类器Key words
image classification/illegal image detection/deep learning/SVM classifier分类
信息技术与安全科学引用本文复制引用
张晨,杜刚,杜雪涛..深度模型集成的不良图像分类[J].北京交通大学学报,2017,41(6):21-26,6.基金项目
教育部-中国移动科研基金(MCM20160102) Joint Fund of Ministry of Education of China and China Mobile (MCM20160102) （MCM20160102）
