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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The best results on Muscle-VCD are 0.86 QS and 0.76 QF, reported in [14].
“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]
Section 7.1 (page unavailable) reports the highest Muscle-VCD-2007 localization scores as QS = 0.90 and QF = 0.82, not 0.86 and 0.76. The 0.86 values visible in Table 7 are detection precision for other teams, not QS scores. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe baseline system has demonstrated state-of-the-art results in recent copy detection research.
“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]
The small performance gap on Muscle-VCD is mainly due to the use of different geometric verification methods.
“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]
Section 7.1 reports Muscle-VCD results and describes reranking with SR-PE verification, but it does not compare the cited method with the citing paper’s baseline or explain a small performance gap. The method description in Section 5 also identifies SR-PE, not WGC, as its verification method. Searches for the performance-gap explanation and WGC found no passage establishing the claimed cause. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Constrained K-means Clustering with Background Knowledge
Wagstaff, K. L., Cardie, C., Rogers, S., & Schrödl, S. (2001) · Cited by 2,469 · see citing works
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 · see citing works
Evaluation campaigns and TRECVid
Smeaton, A. F., Over, P., & Kraaij, W. (2006) · doi:10.1145/1178677.1178722 · Cited by 1,227 · see citing works
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 · see citing works
Efficient near-duplicate detection and sub-image retrieval
Ke, Y. (2004) · Cited by 342 · see citing works
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 · see citing works
Self-citationVideo search reranking through random walk over document-level context graph
Hsu, W. H., Kennedy, L. S., & Chang, S. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291446 · Cited by 226 · see citing works
Comparison of sequence matching techniques for video copy detection
Hampapur, A., Hyun, K.-H., & Bolle, R. M. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.451091 · Cited by 223 · see citing works
Scalable near identical image and shot detection
Chum, O., Philbin, J. F., Isard, M., & Zisserman, A. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282359 · Cited by 198 · see citing works
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Zhang, D., & Chang, S. (2004) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1027527.1027730 · Cited by 197 · see citing works
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
Zhao, W., Ngo, C., Tan, H., & Wu, X. Y. (2007) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2007.898928 · Cited by 169 · see citing works
Self-citationReal-Time Near-Duplicate Elimination for Web Video Search With Content and Context
Wu, X. Y., Ngo, C., Hauptmann, A. G., & Tan, H. (2009) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.2009673 · Cited by 153 · see citing works
Self-citationRobust video signature based on ordinal measure
Hua, X., Chen, X., & Zhang, H.-J. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2004.1418847 · Cited by 152 · see citing works
Robust voting algorithm based on labels of behavior for video copy detection
Law-To, J., Buisson, O., Gouet-Brunet, V., & Boujemaa, N. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180826 · Cited by 143 · see citing works
Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection
Zhao, W., & Ngo, C. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2008900 · Cited by 130 · see citing works
Self-citationVideo Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
Xu, D., & Chang, S. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.129 · Cited by 127 · see citing works
Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval
Jiang, Y., & Ngo, C. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2008.10.002 · Cited by 56 · see citing works
Self-citationEfficiently matching sets of features with random histograms
Dong, W., Wang, Z., Charikar, M., & Li, K. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459384 · Cited by 51 · see citing works
Kennedy, L. S., & Chang, S. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459406 · Cited by 50 · see citing works
Video and image copy detection demo
Law-To, J., Joly, A., Joyeux, L., Boujemaa, N., Buisson, O., & Gouet-Brunet, V. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282295 · Cited by 28 · see citing works
Accelerating near-duplicate video matching by combining visual similarity and alignment distortion
Tan, H., Wu, X. Y., Ngo, C., & Zhao, W. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459506 · Cited by 26 · see citing works
Self-citationKim, H.-S., Lee, J., Liu, H., & Lee, D. (2008) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1386352.1386404 · Cited by 25 · see citing works
Mining TV broadcasts for recurring video sequences
Döhring, I., & Lienhart, R. W. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646432 · Cited by 22 · see citing works
Scene duplicate detection based on the pattern of discontinuities in feature point trajectories
Wu, X., Takimoto, M., Satoh, S., & Adachi, J. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459367 · Cited by 20 · see citing works
An O(nm)-Time Network Simplex Algorithm for the Shortest Path Problem
Goldfarb, D., & Jin, Z. (1999) · Operations Research · doi:10.1287/opre.47.3.445 · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Near-duplicate video retrieval
Liu, J., Huang, Z. H., Cai, H., Shen, H. T., Ngo, C., & Wang, W. (2013) · ACM Computing Surveys · doi:10.1145/2501654.2501658 · Cited by 181 · see citing works
Self-citationPattern-Based Near-Duplicate Video Retrieval and Localization on Web-Scale Videos
Chou, C.-L., Chen, H.-T., & Lee, S.-Y. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2391674 · Cited by 106 · see citing works
Stochastic Multiview Hashing for Large-Scale Near-Duplicate Video Retrieval
Hao, Y., Mu, T., Hong, R., Wang, M., An, N., & Goulermas, J. Y. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2610324 · Cited by 101 · see citing works
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 · see citing works
ViSiL: Fine-Grained Spatio-Temporal Video Similarity Learning
Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., & Kompatsiaris, I. Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00645 · Cited by 82 · see citing works
Partial Copy Detection in Videos: A Benchmark and an Evaluation of Popular Methods
Jiang, Y.-G., & Wang, J. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2530714 · Cited by 72 · see citing works
Temporal Context Aggregation for Video Retrieval with Contrastive Learning
Shao, J., Wen, X., Zhao, B., & Xue, X. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00331 · Cited by 72 · see citing works
Learning spatial-temporal features for video copy detection by the combination of CNN and RNN
Hu, Y., & Lu, X. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.05.013 · Cited by 57 · see citing works
Practical Online Near-Duplicate Subsequence Detection for Continuous Video Streams
Huang, Z. H., Shen, H. T., Shao, J., Cui, B., & Zhou, X. (2010) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2010.2050737 · Cited by 56 · see citing works
Event driven summarization for web videos
Hong, R., Tang, J., Tan, H., Yan, S., Ngo, C., & Chua, T. (2009) · doi:10.1145/1631144.1631154 · Cited by 45 · see citing works
Self-citationLarge-scale video copy retrieval with temporal-concentration SIFT
Zhu, Y., Huang, X., Huang, Q., & Tian, Q. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.09.114 · Cited by 45 · see citing works
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval
Kordopatis-Zilos, G., Tzelepis, C., Papadopoulos, S., Kompatsiaris, I. Y., & Patras, I. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01651-3 · Cited by 37 · see citing works
Compact CNN Based Video Representation for Efficient Video Copy Detection
Wang, L. E., Bao, Y., Li, H., Fan, X., & Luo, Z. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51811-4_47 · Cited by 37 · see citing works
Tag suggestion and localization in user-generated videos based on social knowledge
Ballan, L., Bertini, M., Bimbo, A. D., Meoni, M., & Serra, G. (2010) · doi:10.1145/1878151.1878155 · Cited by 36 · see citing works
Video Similarity and Alignment Learning on Partial Video Copy Detection
Han, Z., He, X., Tang, M., & Lv, Y. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475549 · Cited by 36 · see citing works
Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval
Jiang, C., Huang, K., He, S., Yang, X., Zhang, W., Zhang, X., Cheng, Y., Yang, L., Wang, Q., Xu, F., et al. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475301 · Cited by 36 · see citing works
The 2021 Image Similarity Dataset and ChallengePreprint
Douze, M., Tolias, G., Pizzi, E., Papakipos, Z., Chanussot, L., Radenović, F., Jenícek, T., Maximov, M., Leal-Taixé, L., Elezi, I., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09672 · Cited by 35 · see citing works
Chen, H., Hu, C., Lee, F., Lin, C., Yao, W., Lü, C., & Chen, Q. (2021) · Sensors · doi:10.3390/s21093094 · Cited by 35 · see citing works
Mining near-duplicate graph for cluster-based reranking of web video search results
Huang, Z. H., Hu, B., Cheng, H., Shen, H. T., Liu, H., & Zhou, X. (2010) · ACM Transactions on Information Systems · doi:10.1145/1852102.1852108 · Cited by 25 · see citing works
A multi-embedding neural model for incident video retrieval
Chiang, T.-H., Tseng, Y., & Tseng, Y. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108807 · Cited by 24 · see citing works
Structure Tensor Series-Based Large Scale Near-Duplicate Video Retrieval
Zhou, X., Chen, L., & Zhou, X. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2194481 · Cited by 23 · see citing works
Effective real-scenario Video Copy Detection
Zhang, Y., & Zhang, X. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900252 · Cited by 22 · see citing works
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 · see citing works
Correlation-based retrieval for heavily changed near-duplicate videos
Liu, J., Huang, Z. H., Shen, H. T., & Cui, B. (2011) · ACM Transactions on Information Systems · doi:10.1145/2037661.2037666 · Cited by 21 · see citing works
Learn from Unlabeled Videos for Near-duplicate Video Retrieval
He, X., Pan, Y., Tang, M., Lv, Y., & Peng, Y. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532010 · Cited by 20 · see citing works
Self-Supervised Video Similarity Learning
Kordopatis-Zilos, G., Tolias, G., Tzelepis, C., Kompatsiaris, I. Y., Patras, I., & Papadopoulos, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00504 · Cited by 20 · see citing works
Attention-Based Deep Metric Learning for Near-Duplicate Video Retrieval
Wang, K.-H., Cheng, C.-C., Chen, Y.-L., Song, Y., & Lai, S.-H. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412710 · Cited by 19 · see citing works
Cross media hyperlinking for search topic browsing
Tan, S., Ngo, C., Tan, H., & Pang, L. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072331 · Cited by 19 · see citing works
Self-citationUltrahigh-Speed TV Commercial Detection, Extraction, and Matching
Wu, X., & Satoh, S. (2013) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2013.2248991 · Cited by 17 · see citing works
Efficient Mining of Multiple Partial Near-Duplicate Alignments by Temporal Network
Tan, H., Ngo, C., & Chua, T. (2010) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2010.2077531 · Cited by 17 · see citing works
Self-citationA Fast Partial Video Copy Detection Using KNN and Global Feature Database
Tan, W., Guo, H., & Liu, R. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00053 · Cited by 16 · see citing works
Topical summarization of web videos by visual-text time-dependent alignment
Tan, S., Tan, H., & Ngo, C. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874159 · Cited by 16 · see citing works
Self-citationHierarchical Visualization of Video Search Results for Topic-Based Browsing
Jiang, Y., Wang, J., Wang, Q., Liu, W., & Ngo, C. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2614233 · Cited by 16 · see citing works
Self-citationChiu, C., & Wang, H. (2010) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2010.2087471 · Cited by 16 · see citing works
Fusing heterogeneous modalities for video and image re-ranking
Tan, H., & Ngo, C. (2011) · International Conference on Multimedia Retrieval · doi:10.1145/1991996.1992011 · Cited by 15 · see citing works
Self-citationNear-Duplicate Subsequence Matching Between the Continuous Stream and Large Video Dataset
Chiu, C., Tsai, T., Liou, Y.-C., Han, G.-W., & Chang, H. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2342668 · Cited by 15 · see citing works
Equal but different: a contextual analysis of duplicated videos on YouTube
Rodrigues, T., Benevenuto, F., Almeida, V. A. F., Almeida, J. M., & Gonçalves, M. A. (2010) · Journal of the Brazilian Computer Society · doi:10.1007/s13173-010-0019-x · Cited by 13 · see citing works
Enhancing Single-Frame Supervision for Better Temporal Action Localization
Chen, C., Chen, J., Yang, W., Wang, H., Knittel, J., Zhao, X., Koch, S., Ertl, T., & Liu, S. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3388521 · Cited by 12 · see citing works
Design and evaluation of an effective and efficient video copy detection system
Natsev, A., Hill, M., & Smith, J. R. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583216 · Cited by 12 · see citing works
The 2023 video similarity dataset and challenge
Pizzi, E., Kordopatis-Zilos, G., Patel, H., Postelnicu, G. M., Ravindra, S. N., Gupta, A., Papadopoulos, S., Tolias, G., & Douze, M. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103997 · Cited by 12 · see citing works
VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression
Jo, W., Lim, G., Lee, G., Kim, H., Ko, B., & Choi, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28046 · Cited by 11 · see citing works
Model-based sparse component analysis for multiparty distant speech recognition
(2013) · ACM SIGMultimedia Records · doi:10.1145/2584671.2584677 · Cited by 11 · see citing works
Video Copy Detection Based on Deep CNN Features and Graph-Based Sequence Matching
Zhang, X., Xie, Y., Luan, X., He, J., Zhang, L., & Wu, L. (2018) · Wireless Personal Communications · doi:10.1007/s11277-018-5450-x · Cited by 11 · see citing works
Detection and location of near-duplicate video sub-clips by finding dense subgraphs
Chen, T., Jiang, S., Chu, L., & Huang, Q. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2071967 · Cited by 10 · see citing works
Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval
Liu, Y., Xu, Q., Wen, P., Dai, S., & Huang, Q. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681110 · Cited by 9 · see citing works
Multimodal Video-to-Near-Scene Annotation
Chou, C.-L., Chen, H.-T., & Lee, S.-Y. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2614426 · Cited by 9 · see citing works
A Rotation Invariant Descriptor for Robust Video Copy Detection
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 · see citing works
Pang, L., Zhang, W., Tan, H., & Ngo, C. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2396520 · Cited by 7 · see citing works
Self-citationMultiscale video sequence matching for near-duplicate detection and retrieval
Yang, Y., Tian, Y., & Huang, T. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5862-3 · Cited by 7 · see citing works
3D-CSL: Self-Supervised 3D Context Similarity Learning for Near-Duplicate Video Retrieval
Deng, R., Wu, Q., & Li, Y. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222915 · Cited by 7 · see citing works
Katayama, N., Mo, H., & Satoh, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-17832-0_27 · Cited by 7 · see citing works
Zhang, X., Hua, G., Zhang, L., & Shum, H. (2010) · 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/cvpr.2010.5540042 · Cited by 7 · see citing works
Fast copy detection based on Slice Entropy Scattergraph
Cui, P., Wu, Z., Jiang, S., & Huang, Q. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5583815 · Cited by 7 · see citing works
Large-Scale Near-Duplicate Web Video Retrieval: Challenges and Approaches
Cai, Y., & Yang, L. (2013) · IEEE Multimedia · doi:10.1109/mmul.2013.23 · Cited by 6 · see citing works
Video Retrieval for Everyday Scenes With Common Objects
Zachariah, A., & Rao, P. R. (2023) · International Conference on Multimedia Retrieval · doi:10.1145/3591106.3592239 · Cited by 6 · see citing works
Efficient video segment matching for detecting temporal-based video copies
Chiu, C., Tsai, T., & Hsieh, C.-Y. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.04.036 · Cited by 6 · see citing works
Finding Near-Duplicate Videos in Large-Scale Collections
Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., & Kompatsiaris, I. Y. (2019) · Springer eBooks · doi:10.1007/978-3-030-26752-0_4 · Cited by 6 · see citing works
An Automatic Near-Duplicate Video Data Cleaning Method Based on a Consistent Feature Hash Ring
Qin, Y., Ye, O., & Fu, Y. (2024) · Electronics · doi:10.3390/electronics13081522 · Cited by 4 · see citing works
Video Query Reformulation for Near-Duplicate Detection
Chiu, C., Li, S., & Hsieh, C.-Y. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2207115 · Cited by 5 · see citing works
Video Copy Detection Based on Path Merging and Query Content Prediction
Nan, N., & Liu, G. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2395771 · Cited by 5 · see citing works
Video copy detection by conducting fast searching of inverted files
Liu, M., Po, L.-M., Rehman, Y. A. U., Xu, X., Li, Y., & Feng, L. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6639-4 · Cited by 5 · see citing works
Content-based Video Copy Detection using Binary Object Fingerprints
Liu, M., Po, L.-M., Zhou, C. S., Yuen, W. Y. F., Cheung, H. K., Wong, P. H. W., Luk, H.-T., & Lau, K.-W. (2018) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc.2018.8567827 · Cited by 5 · see citing works
Trajectory-based visualization of web video topics
Cao, J., Ngo, C., Zhang, Y., Zhang, D., & Ma, L. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874309 · Cited by 5 · see citing works
Self-citationChiu, C., Tsai, T., Han, G.-W., Hsieh, C.-Y., & Li, S. (2013) · ACM Transactions on Information Systems · doi:10.1145/2516890 · Cited by 5 · see citing works
The 2023 Video Similarity Dataset and ChallengePreprint
Pizzi, E., Kordopatis-Zilos, G., Patel, H., Postelnicua, G., Ravindra, S. N., Gupta, A., Papadopoulos, S., Tolias, G., & Douze, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4597445 · Cited by 5 · see citing works
Simultaneous Video Retrieval and Alignment
Jo, W., Lim, G., Hwang, Y., Lee, G., Kim, J., Yun, J., Jung, J., & Choi, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3259733 · Cited by 4 · see citing works
Tian, Y., Qian, M., & Huang, T. (2015) · ACM Transactions on Information Systems · doi:10.1145/2699662 · Cited by 4 · see citing works
Video fingerprinting: Past, present, and future
Allouche, M., & Mitrea, M. (2022) · Frontiers in Signal Processing · doi:10.3389/frsip.2022.984169 · Cited by 4 · see citing works
Gradient Ordinal Signature and Fixed-Point Embedding for Efficient Near-Duplicate Video Detection
Liu, H., Lu, H., Wen, Z., & Xue, X. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2168294 · Cited by 4 · see citing works
VERD: Emergence of Product-Based Video E-Commerce Retrieval Dataset from User’s Perspective
Lee, G., Jo, W., & Choi, Y. (2023) · Sensors · doi:10.3390/s23010513 · Cited by 3 · see citing works
Self-supervised Video Copy Localization with Regional Token Representation
Lu, M., Lu, Y., Nie, S., Yang, X., & Zhang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73254-6_2 · Cited by 3 · see citing works
Multi-modal Sign Language Spotting by Multi/One-Shot Learning
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 · see citing works
An Efficient Partial Video Copy Detection for a Large-scale Video Database
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 · see citing works
GANN: A Graph alignment neural network for video partial copy detection
Liu, X., Feng, X., & Pan, P. (2021) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/bigdatasecurityhpscids52275.2021.00044 · Cited by 3 · see citing works
Organizing Video Search Results to Adapted Semantic Hierarchies for Topic-based Browsing
Wang, J., Jiang, Y., Wang, Q., Yang, K., & Ngo, C. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2655012 · Cited by 3 · see citing works
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 · see citing works
Context Encoding for Video Retrieval with Contrastive LearningPreprint
Shao, J., Wen, X., Zhao, B., Wang, C., & Xue, X. (2020) · arXiv (Cornell University) · Cited by 3 · see citing works
Self-supervised Video Retrieval Transformer NetworkPreprint
He, X., Pan, Y., Tang, M., & Lv, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07993 · Cited by 3 · see citing works
A Near-Duplicate Video Cleaning Method Based on AFENet Adaptive Clustering
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 · see citing works
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 · see citing works
Scene Signatures for Unconstrained News Video Stories
Younessian, E., & Rajan, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27355-1_10 · Cited by 2 · see citing works
Pang, L., Tan, S., Tan, H., & Ngo, C. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072467 · Cited by 2 · see citing works
Self-citationVideo retrieval algorithm based on video fingerprints and spatiotemporal information
Liu, W., Jia, K., Wang, Z., & Feng, J. (2014) · doi:10.1109/icosp.2014.7015214 · Cited by 2 · see citing works
Kim, K., Jang, W.-D., & Kim, C. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350753 · Cited by 2 · see citing works
Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization
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 · see citing works
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalPreprint
Kordopatis-Zilos, G., Tzelepis, C., Papadopoulos, S., Kompatsiaris, I. Y., & Patras, I. (2022) · arXiv (Cornell University) · doi:10.5281/zenodo.6833056 · Cited by 2 · see citing works
Frame by Familiar Frame: Understanding Replication in Video Diffusion Models
Rahman, A., Perera, M. V., & Patel, V. M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00274 · Cited by 5 · see citing works
Transformer -based audio-visual features for video copy detection
Wang, Y., Meng, L., Li, S., & Luo, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00578-w · Cited by 1 · see citing works
Evaluating Gaussian Like Image Representations over Local Features
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 · see citing works
A novel signature for fast video retrieval
Haolin, G., & Li, B. (2011) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2011.6099932 · Cited by 1 · see citing works
Byun, S.-W., & Lee, S.-P. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.08.028 · Cited by 1 · see citing works
Ngo, C. (2012) · CRC Press eBooks · doi:10.1201/b11716-17 · Cited by 1 · see citing works
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 · see citing works
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
Approximate Hough transform for video subsequence matching
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
Commercial and Trademark Recognition
BALLAN, M. B. L. (2012) · Auerbach Publications eBooks · doi:10.1201/b11723-11
A framework for associated news story retrieval
Younessian, E. (2013) · doi:10.32657/10356/54903
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
