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CIVR
2006
Springer
139views Image Analysis» more  CIVR 2006»
13 years 11 months ago
Using High-Level Semantic Features in Video Retrieval
Extraction and utilization of high-level semantic features are critical for more effective video retrieval. However, the performance of video retrieval hasn't benefited much d...
Wujie Zheng, Jianmin Li, Zhangzhang Si, Fuzong Lin...
ADVIS
2006
Springer
14 years 1 months ago
Structural and Event Based Multimodal Video Data Modeling
Investments on multimedia technology enable us to store many more reflections of the real world in digital world as videos. By recording videos about real world entities, we carry...
Hakan Öztarak, Adnan Yazici
MM
2004
ACM
142views Multimedia» more  MM 2004»
14 years 1 months ago
Learning query-class dependent weights in automatic video retrieval
Combining retrieval results from multiple modalities plays a crucial role for video retrieval systems, especially for automatic video retrieval systems without any user feedback a...
Rong Yan, Jun Yang 0003, Alexander G. Hauptmann
VCIP
2003
151views Communications» more  VCIP 2003»
13 years 9 months ago
Sports video categorizing method using camera motion parameters
In this paper, we propose a content based video categorizing method focusing broadcasted sports videos using camera motion parameters. We define two new features in the proposed m...
Shinichi Takagi, Shinobu Hattori, Kazumasa Yokoyam...
MIR
2006
ACM
151views Multimedia» more  MIR 2006»
14 years 1 months ago
Evaluation campaigns and TRECVid
The TREC Video Retrieval Evaluation (TRECVid) is an international benchmarking activity to encourage research in video information retrieval by providing a large test collection, ...
Alan F. Smeaton, Paul Over, Wessel Kraaij