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18th International Conference on Pattern Recognition (ICPR'06) Volume 2
An Unsupervised Algorithm for Anchor Shot Detection
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
ASCII Text
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M. De Santo, P. Foggia, C. Sansone, G. Percannella, M. Vento, "An Unsupervised Algorithm for Anchor Shot Detection," Pattern Recognition, International Conference on, vol. 2, pp. 1238-1241, 18th International Conference on Pattern Recognition (ICPR'06) Volume 2, 2006.
 
 
BibTex
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@article{ 10.1109/ICPR.2006.266,
author = {M. De Santo and P. Foggia and C. Sansone and G. Percannella and M. Vento},
title = {An Unsupervised Algorithm for Anchor Shot Detection},
journal ={Pattern Recognition, International Conference on},
volume = {2},
year = {2006},
issn = {1051-4651},
pages = {1238-1241},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.266},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - An Unsupervised Algorithm for Anchor Shot Detection
SN - 1051-4651
SP1238
EP1241
A1 - M. De Santo,
A1 - P. Foggia,
A1 - C. Sansone,
A1 - G. Percannella,
A1 - M. Vento,
PY - 2006
VL - 2
JA - Pattern Recognition, International Conference on
ER -
 
M. De Santo, Università degli Studi di Salerno, Italy
P. Foggia, Università degli Studi di Napoli "Federico II", Italy
C. Sansone, Università degli Studi di Napoli "Federico II", Italy
G. Percannella, Università degli Studi di Salerno, Italy
M. Vento, Università degli Studi di Salerno, Italy
In this paper we present a novel algorithm for anchor shot detection (ASD). ASD is a fundamental step for segmenting news video into stories that is among key issues for achieving efficient treatment of news-based digital libraries. The proposed algorithm firstly uses a clustering method for individuating candidate anchor shots and then employs a two-stage pruning technique for reducing the number of falsely detected anchor shots. Both clustering and pruning are carried out in an unsupervised way. The algorithm has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them.
Citation:
M. De Santo, P. Foggia, C. Sansone, G. Percannella, M. Vento, "An Unsupervised Algorithm for Anchor Shot Detection," icpr, vol. 2, pp.1238-1241, 18th International Conference on Pattern Recognition (ICPR'06) Volume 2, 2006
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