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A novel framework for semantic-based video retrieval

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4 Author(s)
Xiaoming Nan ; Sch. of Inf. & Telecommun. Eng., BUPT, Beijing, China ; Zhicheng Zhao ; Cai, A. ; Xiaohui Xie

In this paper, a novel framework for semantic-based video retrieval is proposed. 15 low-level visual features on different levels are extracted and a supervised SVM classifier is trained for each feature. We have explored early fusion schemes between SIFT and SURF, and evaluated 4 kinds of later fusion strategies. Experiments on TRECVID dataset show that the proposed system is effective and stable.

Published in:

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on  (Volume:4 )

Date of Conference:

20-22 Nov. 2009