DocumentCode :
3441581
Title :
A video retrieval algorithm based on ensemble similarity
Author :
Deng, Li ; Jin, Li-Zuo
Author_Institution :
Key Lab. of Power Station Autom., Shanghai Univ., Shanghai, China
Volume :
3
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
638
Lastpage :
642
Abstract :
This paper proposed an ensemble similarity based method for video retrieval. An ensemble similarity is used to calibrate the similarity between user given query video clip and each video clip in the database: a clip can be treated as an ensemble which consists of a sequence of multiple key frames. By kernel method, in a high dimension space the feature vector represented frames can be assumed to distribute a Gaussian model. Then probabilistic distance between two Gaussians is computed as the similarity value between two video clips. Then video clips in database with the highest similarity are output and submitted to the user. To improve the speed efficiency, an improved algorithm of Chernoff distance and KL divergence is also proposed. The experimental results indicate that the proposed approach achieves superior performance than some existing methods.
Keywords :
Gaussian processes; video retrieval; Chernoff distance; Gaussian model; KL divergence; ensemble similarity based method; feature vector; kernel method; multiple key frame sequence; user given query video clip; video retrieval algorithm; Complexity theory; Industries; Kernel; Lead; Q measurement; ensemble similarity; kernel method; probabilistic distance; video retrieval;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6582-8
Type :
conf
DOI :
10.1109/ICICISYS.2010.5658397
Filename :
5658397
Link To Document :
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