CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
A Generic Framework for Video Annotation via Semi-Supervised Learning
Zhang, Tianzhu1,2; Xu, Changsheng2,3; Zhu, Guangyu2,4; Liu, Si2,3; Lu, Hanqing2,3
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2012-08-01
Volume14Issue:4Pages:1206-1219
SubtypeArticle
AbstractLearning-based video annotation is essential for video analysis and understanding, and many various approaches have been proposed to avoid the intensive labor costs of purely manual annotation. However, there lacks a generic framework due to several difficulties, such as dependence of domain knowledge, insufficiency of training data, no precise localization and inefficacy for large-scale video dataset. In this paper, we propose a novel approach based on semi-supervised learning by means of information from the Internet for interesting event annotation in videos. Concretely, a Fast Graph-based Semi-Supervised Multiple Instance Learning (FGSSMIL) algorithm, which aims to simultaneously tackle these difficulties in a generic framework for various video domains (e. g., sports, news, and movies), is proposed to jointly explore small-scale expert labeled videos and large-scale unlabeled videos to train the models. The expert labeled videos are obtained from the analysis and alignment of well-structured video related text (e. g., movie scripts, web-casting text, close caption). The unlabeled data are obtained by querying related events from the video search engine (e. g., YouTube, Google) in order to give more distributive information for event modeling. Two critical issues of FGSSMIL are: 1) how to calculate the weight assignment for a graph construction, where the weight of an edge specifies the similarity between two data points. To tackle this problem, we propose a novel Multiple Instance Learning Induced Similarity (MILIS) measure by learning instance sensitive classifiers; 2) how to solve the algorithm efficiently for large-scale dataset through an optimization approach. To address this issue, Concave-Convex Procedure (CCCP) and nonnegative multiplicative updating rule are adopted. We perform the extensive experiments in three popular video domains: movies, sports, and news. The results compared with the state-of-the-arts are promising and demonstrate the effectiveness and efficiency of our proposed approach.
KeywordBroadcast Video Concave-convex Procedure (Cccp) Event Detection Graph Internet Multiple Instance Learning Semi-supervised Learning Web-casting Text
WOS HeadingsScience & Technology ; Technology
WOS KeywordLINEAR NEIGHBORHOOD PROPAGATION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000306599400008
Citation statistics
Cited Times:34[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3351
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.Adv Digital Sci Ctr ADSC, Singapore 138632, Singapore
2.China Singapore Inst Digital Media, Singapore 119613, Singapore
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore, Singapore
Recommended Citation
GB/T 7714
Zhang, Tianzhu,Xu, Changsheng,Zhu, Guangyu,et al. A Generic Framework for Video Annotation via Semi-Supervised Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2012,14(4):1206-1219.
APA Zhang, Tianzhu,Xu, Changsheng,Zhu, Guangyu,Liu, Si,&Lu, Hanqing.(2012).A Generic Framework for Video Annotation via Semi-Supervised Learning.IEEE TRANSACTIONS ON MULTIMEDIA,14(4),1206-1219.
MLA Zhang, Tianzhu,et al."A Generic Framework for Video Annotation via Semi-Supervised Learning".IEEE TRANSACTIONS ON MULTIMEDIA 14.4(2012):1206-1219.
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