Title :
A novel framework for semantic-based video retrieval
Author :
Nan, Xiaoming ; Zhao, Zhicheng ; Cai, Anni ; Xie, Xiaohui
Author_Institution :
Sch. of Inf. & Telecommun. Eng., BUPT, Beijing, China
Abstract :
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.
Keywords :
support vector machines; video retrieval; fusion schemes; low level visual features; semantic based video retrieval; supervised SVM classifier; Content based retrieval; Data mining; Detectors; Feature extraction; Histograms; Information retrieval; Layout; Pipelines; Robustness; Vocabulary; TRECVID; concept detection; feature extraction; semantic-based video retrieval;
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
DOI :
10.1109/ICICISYS.2009.5357647
