CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Semantic Feature Mining for Video Event Understanding
Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng
Source PublicationACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
2016-08-01
Volume12Issue:4Pages:55:1-55:22
SubtypeArticle
AbstractContent-based video understanding is extremely difficult due to the semantic gap between low-level vision signals and the various semantic concepts (object, action, and scene) in videos. Though feature extraction from videos has achieved significant progress, most of the previous methods rely only on low-level features, such as the appearance and motion features. Recently, visual-feature extraction has been improved significantly with machine-learning algorithms, especially deep learning. However, there is still not enough work focusing on extracting semantic features from videos directly. The goal of this article is to adopt unlabeled videos with the help of text descriptions to learn an embedding function, which can be used to extract more effective semantic features from videos when only a few labeled samples are available for video recognition. To achieve this goal, we propose a novel embedding convolutional neural network (ECNN). We evaluate our algorithm by comparing its performance on three challenging benchmarks with several popular state-of-the-art methods. Extensive experimental results show that the proposed ECNN consistently and significantly outperforms the existing methods.
KeywordVideo Recognition Event
WOS HeadingsScience & Technology ; Technology
DOI10.1145/2962719
WOS KeywordRECOGNITION ; IMAGES ; TEXT
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61225009 ; National Basic Research Program of China(2012CB316304) ; Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(IDHT20140224) ; 61303173 ; 61432019 ; 61572498 ; 61532009 ; 61472379 ; 61572296)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000382877500009
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12631
Collection模式识别国家重点实验室_多媒体计算与图形学
AffiliationNational Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng. Semantic Feature Mining for Video Event Understanding[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2016,12(4):55:1-55:22.
APA Yang, Xiaoshan,Zhang, Tianzhu,&Xu, Changsheng.(2016).Semantic Feature Mining for Video Event Understanding.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,12(4),55:1-55:22.
MLA Yang, Xiaoshan,et al."Semantic Feature Mining for Video Event Understanding".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 12.4(2016):55:1-55:22.
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