Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCSVT.2010.2077531
Title: Efficient mining of multiple partial near-duplicate alignments by temporal network
Authors: Tan, H.-K.
Ngo, C.-W.
Chua, T.-S. 
Keywords: Keyword matching
partial near-duplicate
temporal graph
Issue Date: 2010
Source: Tan, H.-K., Ngo, C.-W., Chua, T.-S. (2010). Efficient mining of multiple partial near-duplicate alignments by temporal network. IEEE Transactions on Circuits and Systems for Video Technology 20 (11) : 1486-1498. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSVT.2010.2077531
Abstract: This paper considers the mining and localization of near-duplicate segments at arbitrary positions of partial near-duplicate videos in a corpus. Temporal network is proposed to model the visual-temporal consistency between video sequence by embedding temporal constraints as directed edges in the network. Partial alignment is then achieved through network flow programming. To handle multiple alignments, we consider two properties of network structure: conciseness and divisibility, to ensure that the mining is efficient and effective. Frame-level matching is further integrated in the temporal network for alignment verification. This results in an iterative alignment-verification procedure to fine tune the localization of near-duplicate segments. The scalability of frame-level matching is enhanced by exploring visual keyword matching algorithms. We demonstrate the proposed work for mining partial alignments from two months of broadcast videos and across six TV sources. © 2006 IEEE.
Source Title: IEEE Transactions on Circuits and Systems for Video Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/39891
ISSN: 10518215
DOI: 10.1109/TCSVT.2010.2077531
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

11
checked on Dec 7, 2017

WEB OF SCIENCETM
Citations

9
checked on Nov 29, 2017

Page view(s)

81
checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.