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University of Bradford at TRECVID 2008 content based copy detection task

Chen, J and Jiang, J (2008) University of Bradford at TRECVID 2008 content based copy detection task 2008 TREC Video Retrieval Evaluation Notebook Papers.

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1. Briefly, what approach or combination of approaches did you test in each of your submitted runs? (please use the run id from the overall results table NIST returns) BradfordU_FhG.v.Juan: we present a novel method for spatial-temporal video copy detection based on adaptive masking. 2. What if any significant differences (in terms of what measures) did you find among the runs? No. 3. Based on the results, can you estimate the relative contribution of each component of your system/approach to its effectiveness? Firstly, a dedicated video analysis is implemented for input videos, which ensures the accurate detection of complicated distortions query videos may undergo. Secondly, simple signatures are extracted for the benefit of time and space efficiency, and the frame mask is generated adaptively to reduce video temporal redundancy. Thirdly, a matching process is implemented to find video copies. 4. Overall, what did you learn about runs/approaches and the research question(s) that motivated them? The proposed video copy detection framework is effective, and robust against spatial and temporal variations.

Item Type: Article
Authors :
Chen, J
Date : 1 January 2008
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:20
Last Modified : 16 Jan 2019 18:43

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