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

doi:10.1109/TCSVT.2020.2992276

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 视频内容安全团队

	Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection
	Ding, Xinmiao 9; Li, Bing 1,8; Li, Yangxi 7; Guo, Wen 9; Liu, Yao 6; Xiong, Weihua 5; Hu, Weiming 2,3,4
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2021-03-01
卷号	31 期号:3 页码:1222-1233
关键词	Feature extraction Prototypes Visualization Support vector machines Machine learning Streaming media Spectrogram Representative prototype selection objectionable video recognition deep learning
ISSN号	1051-8215
DOI	10.1109/TCSVT.2020.2992276
通讯作者	Li, Bing(bli@nlpr.ia.ac.cn)
英文摘要	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.
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
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000626532100030
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44073]
专题	自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者	Li, Bing
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit NLPR, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100039, Peoples R China 3.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 5.PeopleAI Inc, Beijing 100190, Peoples R China 6.Beijing Inst Appl Sci & Technol, Beijing 100091, Peoples R China 7.Coordinat Ctr China CNCERT CC, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China 8.Peoples Daily Online, State Key Lab Commun Content Cognit, Beijing 100733, Peoples R China 9.Shandong Technol & Business Univ, Sch Informat & Elect Engn, Yantai 264005, Peoples R China
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.