Hierarchical latent concept discovery for video event detection

Li, Chao, Huang, Zi, Yang, Yang, Cao, Jiewei, Sun, Xiaoshuai and Shen, Heng Tao (2017) Hierarchical latent concept discovery for video event detection. IEEE Transactions on Image Processing, 26 5: 2149-2162. doi:10.1109/TIP.2017.2670782

Author Li, Chao
Huang, Zi
Yang, Yang
Cao, Jiewei
Sun, Xiaoshuai
Shen, Heng Tao
Title Hierarchical latent concept discovery for video event detection
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
Publication date 2017-05-01
Sub-type Article (original research)
DOI 10.1109/TIP.2017.2670782
Open Access Status Not yet assessed
Volume 26
Issue 5
Start page 2149
End page 2162
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Subject 1712 Software
1704 Computer Graphics and Computer-Aided Design
Abstract Semantic information is important for video event detection. How to automatically discover, model, and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modeling from video data. Specially, different from most of the approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i.e., temporal sequence relationships of static-visual concepts) in segment-level. The unified model not only enables a discriminative and descriptive representation for videos, but also alleviates error propagation problem from video representation to event modeling existing in previous methods. A max-margin framework is employed to learn the model. Extensive experiments on four challenging video event datasets, i.e., MED11, CCV, UQE50, and FCVID, have been conducted to demonstrate the effectiveness of the proposed method.
Keyword Event detection
Latent concepts
Semantic information
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID FT130101530
61572108 | 61632007

ZYGX2014Z007 | ZYGX2015J055
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
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