Visual Event Detection using Multi-Dimensional Concept Dynamics
S. Ebadollahi,Lexing Xie,Shih-Fu Chang,John R. Smith +3 more
- 09 Jul 2006
- pp 881-884
TL;DR: A novel framework is introduced for visual event detection and results indicate that such a data-driven statistical approach is in fact effective in detecting different visual events such as exiting car, riot, and airplane flying.
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Abstract: A novel framework is introduced for visual event detection. Visual events are viewed as stochastic temporal processes in the semantic concept space. In this concept-centered approach to visual event modeling, the dynamic pattern of an event is modeled through the collective evolution patterns of the individual semantic concepts in the course of the visual event. Video clips containing different events are classified by employing information about how well their dynamics in the direction of each semantic concept matches those of a given event. Results indicate that such a data-driven statistical approach is in fact effective in detecting different visual events such as exiting car, riot, and airplane flying.
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Citations
•Book
Concept-Based Video Retrieval
Cees G. M. Snoek,Marcel Worring +1 more
- 26 May 2009
TL;DR: This paper presents a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human–computer interaction and lays down the anatomy of a concept-based video search engine.
IBM Research TRECVID 2004 Video Retrieval System.
Arnon Amir,Janne Argillander,Marco Berg,Shih-Fu Chang,Martin Franz,Winston H. Hsu,Giridharan Iyengar,John R. Kender,Lyndon Kennedy,Ching-Yung Lin,Milind Naphade,Apostol Natsev,John R. Smith,Jelena Tesic,Gang Wu,Rong Yan,Donqing Zhang +16 more
- 01 Jan 2004
TL;DR: In the NIST TRECVID-2004 evaluation as discussed by the authors, shot boundary detection, high-level feature detection, story segmentation, and search were all performed by the same team.
Semantic Model Vectors for Complex Video Event Recognition
TL;DR: This study reveals that the proposed semantic model vectors representation outperforms-and is complementary to-other low-level visual descriptors for video event modeling, and validates it not only as the best individual descriptor, outperforming state-of-the-art global and local static features as well as spatio-temporal HOG and HOF descriptors, but also as the most compact.
Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
Dong Xu,Shih-Fu Chang +1 more
TL;DR: This work systematically study the problem of event recognition in unconstrained news video sequences by adopting the discriminative kernel-based method for which video clip similarity plays an important role and develops temporally aligned pyramid matching (TAPM) for measuring video similarity.
Video event detection using motion relativity and visual relatedness
Feng Wang,Yu-Gang Jiang,Chong-Wah Ngo +2 more
- 26 Oct 2008
TL;DR: A new motion feature, namely Expanded Relative Motion Histogram of Bag-of-Visual-Words (ERMH-BoW) to employ motion relativity and visual relatedness for event detection and to alleviate the visual word correlation problem in BoW is proposed.
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