Abstract
This paper proposes a near-duplicate video clustering method based on temporal gradient pattern (TGP). Most related work that extract feature from video ignored temporal changes of video. To solve this problem we propose feature extraction method that used temporal changes of video that is the most important feature at video. Temporal gradient pattern is extracted from a shot video and patterned by temporal changes of frames within a shot video. We proposed the method that can cluster near-duplicate video effectively and quickly using the temporal gradient pattern. Experiments have performed for speed, feature extraction and video clustering evaluation. Proposed method shows good performance to represent videos using proposed feature extraction method, clustering method can obtain good performance and speed also is good.
| Original language | English |
|---|---|
| Title of host publication | 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 |
| Pages | 3186-3189 |
| Number of pages | 4 |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 2010 Nov 15 |
| Event | 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China Duration: 2010 Jul 11 → 2010 Jul 14 |
Other
| Other | 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 |
|---|---|
| Country | China |
| City | Qingdao |
| Period | 10/7/11 → 10/7/14 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computational Theory and Mathematics
- Human-Computer Interaction
Cite this
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Temporal gradient pattern for the near-duplicate video clustering. / Lee, Hyundeok; Byun, Hyeran.
2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. Vol. 6 2010. p. 3186-3189 5580710.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Temporal gradient pattern for the near-duplicate video clustering
AU - Lee, Hyundeok
AU - Byun, Hyeran
PY - 2010/11/15
Y1 - 2010/11/15
N2 - This paper proposes a near-duplicate video clustering method based on temporal gradient pattern (TGP). Most related work that extract feature from video ignored temporal changes of video. To solve this problem we propose feature extraction method that used temporal changes of video that is the most important feature at video. Temporal gradient pattern is extracted from a shot video and patterned by temporal changes of frames within a shot video. We proposed the method that can cluster near-duplicate video effectively and quickly using the temporal gradient pattern. Experiments have performed for speed, feature extraction and video clustering evaluation. Proposed method shows good performance to represent videos using proposed feature extraction method, clustering method can obtain good performance and speed also is good.
AB - This paper proposes a near-duplicate video clustering method based on temporal gradient pattern (TGP). Most related work that extract feature from video ignored temporal changes of video. To solve this problem we propose feature extraction method that used temporal changes of video that is the most important feature at video. Temporal gradient pattern is extracted from a shot video and patterned by temporal changes of frames within a shot video. We proposed the method that can cluster near-duplicate video effectively and quickly using the temporal gradient pattern. Experiments have performed for speed, feature extraction and video clustering evaluation. Proposed method shows good performance to represent videos using proposed feature extraction method, clustering method can obtain good performance and speed also is good.
UR - http://www.scopus.com/inward/record.url?scp=78149320261&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78149320261&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2010.5580710
DO - 10.1109/ICMLC.2010.5580710
M3 - Conference contribution
AN - SCOPUS:78149320261
SN - 9781424465262
VL - 6
SP - 3186
EP - 3189
BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
ER -