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Scalable detection of partial near-duplicate videos by visual-temporal consistency
Open accessAbstract
Following the exponential growth of social media, there now exist huge repositories of videos online. Among the huge volumes of videos, there exist large numbers of near-duplicate videos. Most existing techniques either focus on the fast retrieval of full copies or near-duplicates, or consider localization in a heuristic manner. This paper considers the scalable detection and localization of partial near-duplicate videos by jointly considering visual similarity and temporal consistency. Temporal constraints are embedded into a network structure as directed edges. Through the structure, partial alignment is novelly converted into a network flow problem where highly efficient solutions exist. To precisely decide the boundaries of the overlapping segments, pair-wise constraints generated from keypoint matching can be added to the network to iteratively refine the localization result. We demonstrate the effectiveness of partial alignment for three different tasks. The first task links partial segments in full-length movies to videos crawled from YouTube. The second task performs fast web video search, while the third performs near-duplicate shot and copy detection. The experimental result demonstrates the effectiveness and efficiency of the proposed method compared to state-of-the-art techniques.
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“To our knowledge the best results achieved on this dataset are 0.86 and 0.76 respectively for the two criteria, reported in [14]”
From VCDB: A Large-Scale Database for Partial Copy Detection in Videos (2014) · cites this paper as [14]
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“We further benchmark a baseline system on VCDB, which has demonstrated state-of-the-art results in recent copy detection research.”
From VCDB: A Large-Scale Database for Partial Copy Detection in Videos (2014) · cites this paper as [14]
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“The small performance gap is mainly due to the use of different geometric verification methods. An improved version of the WGC was used in [14], while our baseline uses the standard WGC.”
From VCDB: A Large-Scale Database for Partial Copy Detection in Videos (2014) · cites this paper as [14]
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Network Flows: Theory, Algorithms, and Applications.
Smith, D. K., Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1994) · Journal of the Operational Research Society · doi:10.2307/2583863 · Cited by 8,121
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237
Constrained K-means Clustering with Background Knowledge
Wagstaff, K. L., Cardie, C., Rogers, S., & Schrödl, S. (2001) · Cited by 2,469
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901
Evaluation campaigns and TRECVid
Smeaton, A. F., Over, P., & Kraaij, W. (2006) · doi:10.1145/1178677.1178722 · Cited by 1,227
An efficient parts-based near-duplicate and sub-image retrieval system
Ke, Y., Sukthankar, R., & Huston, L. (2004) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1027527.1027729 · Cited by 364
Efficient near-duplicate detection and sub-image retrieval
Ke, Y. (2004) · Cited by 342
Practical elimination of near-duplicates from web video search
Wu, X., Hauptmann, A. G., & Ngo, C.-W. (2007) · Proceedings of the 15th ACM international conference on Multimedia · doi:10.1145/1291233.1291280 · Cited by 340
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Self-citationVCDB: A Large-Scale Database for Partial Copy Detection in Videos
Jiang, Y.-G., Jiang, Y., & Wang, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10593-2_24 · Cited by 82
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Han, Z., He, X., Tang, M., & Lv, Y. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475549 · Cited by 36
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Chen, H., Hu, C., Lee, F., Lin, C., Yao, W., Lü, C., & Chen, Q. (2021) · Sensors · doi:10.3390/s21093094 · Cited by 35
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He, S., Yang, X., Jiang, C., Liang, G., Zhang, W., Pan, T., Wang, Q., Xu, F., Li, C., Liu, J., et al. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.02041 · Cited by 23
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Chen, T., Jiang, S., Chu, L., & Huang, Q. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2071967 · Cited by 10
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Jiang, S., Su, L., Huang, Q., Cui, P., & Wu, Z. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3501-3_46 · Cited by 8
Pang, L., Zhang, W., Tan, H., & Ngo, C. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2396520 · Cited by 7
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Nan, N., & Liu, G. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2395771 · Cited by 5
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Liu, L., Zhou, W., Zhao, W., Hu, H., & Li, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25085-9_15 · Cited by 3
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Luo, Z., Zhang, L., Lai, J., Wang, X., & Tang, C. (2023) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom61073.2023.00024 · Cited by 3
GANN: A Graph alignment neural network for video partial copy detection
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Self-citationXu, Y., Price, T., Monrose, F., & Frahm, J. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.182 · Cited by 3
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Shao, J., Wen, X., Zhao, B., Wang, C., & Xue, X. (2020) · arXiv (Cornell University) · Cited by 3
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Fu, Y., Duan, R., & Ye, O. (2023) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp58490.2023.10248727 · Cited by 2
iESTA: Instance-Enhanced Spatial–Temporal Alignment for Video Copy Localization
Ding, X., Lou, J., Luo, W., Liu, Y., Li, B., & Hu, W. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3517664 · Cited by 2
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Younessian, E., & Rajan, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27355-1_10 · Cited by 2
Pang, L., Tan, S., Tan, H., & Ngo, C. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072467 · Cited by 2
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Liu, W., Jia, K., Wang, Z., & Feng, J. (2014) · doi:10.1109/icosp.2014.7015214 · Cited by 2
Kim, K., Jang, W.-D., & Kim, C. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350753 · Cited by 2
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Liu, Z., Yu, X., Wang, R., Ye, S., Ma, Z., Dong, J., He, S., Qian, F., Zhang, X., Zimmermann, R., et al. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612002 · Cited by 2
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Su, Y.-C., Wu, G.-L., Chiu, T.-H., Hsu, W. H., & Chang, K.-W. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.23 · Cited by 1
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Haolin, G., & Li, B. (2011) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2011.6099932 · Cited by 1
Byun, S.-W., & Lee, S.-P. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.08.028 · Cited by 1
Ngo, C. (2012) · CRC Press eBooks · doi:10.1201/b11716-17 · Cited by 1
Self-citationLarge-Scale Video Search with Efficient Temporal Voting StructurePreprint
Esen, E., Özkan, S., & Atıl, İ. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07160 · Cited by 1
Video Sequence Identification in TV Broadcasts
Schoeffmann, K., & Boeszoermenyi, L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-17832-0_13
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Chiu, C., Liou, Y.-C., & Tsai, T. (2014) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew.2014.6890627
Scalable near-duplicate video stream monitoring
Chiu, C., Tsai, T., & Hsieh, C.-Y. (2012) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2012.6473444
Fast video search on recurring segments
Esen, E., Özkan, S., & Atıl, İ. (2016) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2016.7496057
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BALLAN, M. B. L. (2012) · Auerbach Publications eBooks · doi:10.1201/b11723-11
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Younessian, E. (2013) · doi:10.32657/10356/54903
Metadata
- Authors
- Tan, H.-K., Ngo, C.-W., Hong, R., & Chua, T.-S.
- Affiliations
- City University of Hong Kong (HK)
- National University of Singapore (SG)
- Venue
- Proceedings of the 17th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2009-10-19
- Pages
- 145-154
- OpenAlex
- W2096395091
- Cited by
- 110
- References
- 29
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 11 · 2022: 7 · 2021: 8 · 2020: 1 · 2019: 3 · 2018: 5 · 2017: 1 · 2016: 8 · 2015: 6 · 2014: 6 · 2013: 7 · 2012: 11
- FWCI
- 8.71
- Type
- conference paper
- Language
- en
- Open access
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- National University of Singapore (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- temporal coherence, partial alignment, network flow, feature point matching, shot detection, copy detection, social media videos
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