Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection

doi:10.1109/TCSVT.2020.2992276

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection
	Ding, Xinmiao 1; Li, Bing2,3 ; Li, Yangxi 4; Guo, Wen 1; Liu, Yao 5; Xiong, Weihua 6; Hu, Weiming7,8,9
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
	2021-03-01
卷号	31 期号:3 页码:1222-1233
通讯作者	Li, Bing(bli@nlpr.ia.ac.cn)
摘要	To protect underage people from accessing objectionable videos in the Internet, an effective objectionable video recognition algorithm is necessary for web filtering. Recently, the multi-instance learning has been introduced for objectionable video recognition and achieves impressive results. However, hand-crafted features as well as redundant and noisy frames in objectionable videos become an intractable problem that inevitably degrades the recognition performance. In this paper, we propose a novel representative prototype selection algorithm embedding deep multi-instance representation learning. In the proposed method, an improved convolutional neural network is designed for multimodal multi-instance feature learning and a self-expressive dictionary learning model based on sparse and low rank constraint is designed to select the representative prototypes from each subspace of instances. Then the bag-level feature is constructed via mapping the bag to the selected prototypes. Experiments on three objectionable video sets show the effectiveness of our method for objectionable video recognition.
关键词	Feature extraction Prototypes Visualization Support vector machines Machine learning Streaming media Spectrogram Representative prototype selection objectionable video recognition deep learning
DOI	10.1109/TCSVT.2020.2992276
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation[JQ18018] ; Beijing Natural Science Foundation[L172051] ; Natural Science Foundation of China[61876100] ; Natural Science Foundation of China[U1803119] ; Natural Science Foundation of China[U1736106] ; Natural Science Foundation of China[61906192] ; Natural Science Foundation of China[61902401] ; Natural Science Foundation of China[61572296] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[61772225] ; Natural Science Foundation of China[61801269] ; NSFC-General Technology Collaborative Fund for Basic Research[U1936204] ; NSFC-General Technology Collaborative Fund for Basic Research[U1636218] ; Science and Technology Service Network Initiative, CAS[KFJ-STS-SCYD-317] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; National Natural Science Foundation of Guangdong[2018B030311046] ; Shandong Provincial Science Technology Support Program of Youth Innovation Team in Colleges[2019KJN041] ; Youth Innovation Promotion Association, CAS
项目资助者	Beijing Natural Science Foundation ; Natural Science Foundation of China ; NSFC-General Technology Collaborative Fund for Basic Research ; Science and Technology Service Network Initiative, CAS ; Key Research Program of Frontier Sciences, CAS ; National Natural Science Foundation of Guangdong ; Shandong Provincial Science Technology Support Program of Youth Innovation Team in Colleges ; Youth Innovation Promotion Association, CAS
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000626532100030
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44074
专题	多模态人工智能系统全国重点实验室_视频内容安全
通讯作者	Li, Bing
作者单位	1.Shandong Technol & Business Univ, Sch Informat & Elect Engn, Yantai 264005, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit NLPR, Inst Automat, Beijing 100190, Peoples R China 3.Peoples Daily Online, State Key Lab Commun Content Cognit, Beijing 100733, Peoples R China 4.Coordinat Ctr China CNCERT CC, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China 5.Beijing Inst Appl Sci & Technol, Beijing 100091, Peoples R China 6.PeopleAI Inc, Beijing 100190, Peoples R China 7.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 8.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 9.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100039, Peoples R China
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Ding, Xinmiao,Li, Bing,Li, Yangxi,et al. Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2021,31(3):1222-1233.
APA	Ding, Xinmiao.,Li, Bing.,Li, Yangxi.,Guo, Wen.,Liu, Yao.,...&Hu, Weiming.(2021).Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,31(3),1222-1233.
MLA	Ding, Xinmiao,et al."Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 31.3(2021):1222-1233.