Event Retrieval in Large Video Collections with Circulant Temporal Encoding
Open accessAbstract
This paper presents an approach for large-scale event retrieval. Given a video clip of a specific event, eg, the wedding of Prince William and Kate Middleton, the goal is to retrieve other videos representing the same event from a dataset of over 100k videos. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. Furthermore, we extend product quantization to complex vectors in order to compress our descriptors, and to compare them in the compressed domain. Our method outperforms the state of the art both in search quality and query time on two large-scale video benchmarks for copy detection, Trecvid and CCWeb. Finally, we introduce a challenging dataset for event retrieval, EVVE, and report the performance on this dataset.
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None of the existing definitions addresses the retrieval of same incident videos.
“However, there does not seem to be strong consensus among researchers about which videos are considered near-duplicate videos and none of the existing definitions addresses the retrieval of same incident videos ; in this paper, we attempt to address these issues and provide solid definitions for all types of associations between videos related to the FIVR problem.”
From FIVR: Fine-Grained Incident Video Retrieval (2019) · cites this paper as [35]
The Revaud et al. pipeline implemented on VCDB observed slightly worse results than the adopted baseline.
“We also implemented this pipeline on VCDB but observed slightly worse results than the adopted baseline.”
From VCDB: A Large-Scale Database for Partial Copy Detection in Videos (2014) · cites this paper as [16]
The Revaud et al. pipeline's worse results are probably because it was designed for similar video event retrieval, emphasizing similar semantics rather than the same visual patterns, and did not enforce strong geometric consistency.
“This is probably because the approach was designed for similar video event retrieval, which emphasizes more on similar semantics, not necessarily the same visual patterns, and therefore did not enforce strong geometric consistency of the matched local feature points.”
From VCDB: A Large-Scale Database for Partial Copy Detection in Videos (2014) · cites this paper as [16]
There is no strong consensus among researchers about which videos are considered near-duplicate videos.
“However, there does not seem to be strong consensus among researchers about which videos are considered near-duplicate videos and none of the existing definitions addresses the retrieval of same incident videos ; in this paper, we attempt to address these issues and provide solid definitions for all types of associations between videos related to the FIVR problem.”
From FIVR: Fine-Grained Incident Video Retrieval (2019) · cites this paper as [35]
EVVE's definition of related videos is much more fuzzy, and it contains annotations only for videos from the same event, not for near-duplicates.
“In EVVE, the definition of the related videos is much more fuzzy, and additionally, the dataset contains annotations only for videos from the same event, and not for its near-duplicates.”
From FIVR: Fine-Grained Incident Video Retrieval (2019) · cites this paper as [35]
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
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,079 · see citing works
Visual object tracking using adaptive correlation filters
Bolme, D., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539960 · Cited by 3,406 · see citing works
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 1× · see citing works
“The tradeoff between search quality, speed and memory usage is optimized with the product quantization technique [9], which is extended to complex vectors in order to compare our descriptors in the compressed Fourier domain.”
MethodSelf-citationObject retrieval with large vocabularies and fast spatial matching
Philbin, J. F., Chum, O., Isard, M., Šivic, J., & Zisserman, A. (2007) · 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.2007.383172 · Cited by 2,931 · see citing works
Improving the Fisher Kernel for Large-Scale Image Classification
Perronnin, F., Sánchez, J., & Mensink, T. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_11 · Cited by 2,588 · see citing works
Exploiting the Circulant Structure of Tracking-by-Detection with Kernels
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33765-9_50 · Cited by 2,310 · see citing works
Three things everyone should know to improve object retrieval
Arandjelović, R., & Zisserman, A. (2012) · 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.2012.6248018 · Cited by 1,391 · 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
Sampling Strategies for Bag-of-Features Image Classification
Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927 · see citing works
Negative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening
Jeǵou, H., & Chum, O. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_55 · Cited by 391 · 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
Multiple feature hashing for real-time large scale near-duplicate video retrieval
Song, J., Yang, Y., Huang, Z., Shen, H. T., & Hong, R. (2011) · Proceedings of the 19th ACM international conference on Multimedia · doi:10.1145/2072298.2072354 · Cited by 334 · see citing works
Correlation Pattern Recognition
Kumar, B. V. K. V., Mahalanobis, A., & Juday, R. D. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511541087 · Cited by 322 · see citing works
Law-To, J., Chen, L., Joly, A., Ivan Laptev, A., Buisson, O., Gouet-Brunet, V., Boujemaa, N., & Stentiford, F. W. M. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282336 · Cited by 273 · see citing works
Correlation pattern recognition
(2006) · Choice Reviews Online · doi:10.5860/choice.44-1544 · Cited by 255 · see citing works
Video watermarking system for broadcast monitoring
Kalker, T., Depovere, G. F. G., Haitsma, J., & Maes, M. J. (1999) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.344661 · Cited by 213 · see citing works
\nTrecvid 2012 - an overview of the goals, tasks, data, evaluation mechanisms and metrics
Over, P. L., Awad, G., Michel, M., Fiscus, J., Sanders, G., Shaw, B., Kraaij, W., Smeaton, A. F., & Quéot, G. (2013) · Radboud Repository (Radboud University) · Cited by 177 · see citing works
Exact Acceleration of Linear Object Detectors
Dubout, C., & Fleuret, F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_22 · Cited by 133 · see citing works
Video copy detection by fast sequence matching
Yeh, M.-C., & Cheng, K.-T. T. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646449 · Cited by 64 · see citing works
Compact Video Description for Copy Detection with Precise Temporal Alignment
Douze, M., Jégou, H., Schmid, C., & Pérez, P. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15549-9_38 · Cited by 53 · see citing works
Self-citationTiny Videos: A Large Data Set for Nonparametric Video Retrieval and Frame Classification
Karpenko, A., & Aarabi, P. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.118 · Cited by 44 · see citing works
Hamming embedding similarity-based image classification
Jain, M., Benmokhtar, R., Jeǵou, H., & Gros, P. (2012) · International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324820 · Cited by 23 · 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
Flagged first, then by citations
3 of the 27 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753 · see citing works
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration
Li, Y., & Zhu, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_18 · Cited by 1,461 · see citing works
Rank Pooling for Action Recognition
Fernando, B., Gavves, E., Jose Oramas, M., Ghodrati, A., & Tuytelaars, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2558148 · Cited by 306 · see citing works
Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder
Song, J., Zhang, H., Li, X., Gao, L., Wang, M., & Hong, R. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2814344 · Cited by 279 · see citing works
Temporal Cycle-Consistency Learning
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., & Zisserman, A. (2019) · 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.2019.00190 · Cited by 263 · see citing works
Efficient Feature Extraction, Encoding, and Classification for Action Recognition
Kantorov, V., & Laptev, I. (2014) · 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.2014.332 · Cited by 241 · see citing works
3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding
Gupta, A., Martínez, J., Little, J. J., & Woodham, R. J. (2014) · 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.2014.333 · Cited by 117 · see citing works
Wang, J., Wang, J., Song, J., Xu, X.-S., Shen, H. T., & Li, S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2324592 · Cited by 118 · see citing works
Beyond Hard Negative Mining: Efficient Detector Learning via Block-Circulant Decomposition
Henriques, J. F., Carreira, J., Caseiro, R., & Batista, J. P. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.343 · Cited by 109 · see citing works
Zhang, H., Wang, M., Hong, R., & Chua, T. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964308 · Cited by 91 · see citing works
VCDB: 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
FIVR: Fine-Grained Incident Video Retrieval
Kordopatis-Zilos, G., Papadopoulos, S., Patras, I., & Kompatsiaris, I. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2905741 · Cited by 78 · see citing works
“clude CC_WEB_VIDEO [49], UQ_VIDEO [42], MUSCLEVCD [30], TRECVID-CBCD 2011 [29], VCDB [21] and EVVE [35].”
BackgroundMethodInfluentialPartial 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
Event-Driven Semantic Concept Discovery by Exploiting Weakly Tagged Internet Images
Chen, J., Cui, Y., Ye, G., Liu, D., & Chang, S. (2014) · International Conference on Multimedia Retrieval · doi:10.1145/2578726.2578729 · Cited by 65 · see citing works
Automatic Visual Concept Learning for Social Event Understanding
Yang, X., Zhang, T., Xu, C., & Hossain, M. S. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2393635 · Cited by 60 · see citing works
LAMV: Learning to Align and Match Videos with Kernelized Temporal Layers
Baraldi, L., Douze, M., Cucchiara, R., & Jeǵou, H. (2018) · 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.2018.00814 · Cited by 54 · see citing works
Self-citationOriented pooling for dense and non-dense rotation-invariant features
Zhao, W.-L., Gravier, G., & Jeǵou, H. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.99 · Cited by 52 · see citing works
SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval
Jiang, Q.-Y., He, Y., Li, G., Lin, J., Li, L., & Li, W.-J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00538 · Cited by 49 · see citing works
Cooper, N., Bernal-Cárdenas, C., Chaparro, O., Moran, K., & Poshyvanyk, D. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00091 · Cited by 45 · see citing works
Hierarchical Video-Moment Retrieval and Step-Captioning
Zala, A., Cho, J., Kottur, S., Chen, X., Oğuz, B., Mehdad, Y., & Bansal, M. (2023) · 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/cvpr52729.2023.02208 · Cited by 45 · see citing works
Enhancing Video Event Recognition Using Automatically Constructed Semantic-Visual Knowledge Base
Zhang, X., Yang, Y., Zhang, Y., Luan, H., Li, J., Zhang, H., & Chua, T. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2449660 · Cited by 38 · 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
Circulant Temporal Encoding for Video Retrieval and Temporal Alignment
Douze, M., Revaud, J., Verbeek, J. J., Jeǵou, H., & Schmid, C. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0875-0 · Cited by 37 · see citing works
Self-citationTemporal Matching Kernel with Explicit Feature Maps
Poullot, S., Shunsuke, T., Nguyen, A. P., Jeǵou, H., & Satoh, S. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806228 · 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
Self-citationContent-Based Video Big Data Retrieval with Extensive Features and Deep Learning
Phan, T., Phan, A.-C., Cao, H.-P., & Trieu, T.-N. (2022) · Applied Sciences · doi:10.3390/app12136753 · Cited by 38 · see citing works
Zero-Aliasing Correlation Filters for Object Recognition
Fernandez, J. A., Boddeti, V. N., Rodríguez, A., & Kumar, B. V. K. V. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2375215 · Cited by 33 · see citing works
Stable Hyper-pooling and Query Expansion for Event Detection
Douze, M., Revaud, J., Schmid, C., & Jeǵou, H. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.229 · Cited by 33 · see citing works
Self-citationMulti-Objective Optimization for Multimodal Visualization
Kalamaras, I., Drosou, A., & Tzovaras, D. K. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2316473 · Cited by 32 · see citing works
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting
Gao, Z., Hua, G., Zhang, D., Jojić, N., Wang, L., Xue, J., & Zheng, N. (2017) · 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.2017.227 · Cited by 28 · see citing works
Double-Bit Quantization and Index Hashing for Nearest Neighbor Search
Xie, H., Mao, Z., Zhang, Y., Deng, H., Yan, C. C., & Chen, Z. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2872898 · 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
Bridge inspection component registration for damage evolution
Bianchi, E., Sakib, N., Woolsey, C. A., & Hebdon, M. H. (2022) · Structural Health Monitoring · doi:10.1177/14759217221083647 · Cited by 22 · see citing works
Text2Video: An End-to-end Learning Framework for Expressing Text With Videos
Yang, X., Zhang, T., & Xu, C. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2807588 · Cited by 21 · see citing works
Extracting Key Segments of Videos for Event Detection by Learning From Web Sources
Song, H., Wu, X., Yu, W., & Jia, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2763322 · Cited by 20 · 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
Chattopadhyay, C., & Das, S. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0189 · Cited by 20 · see citing works
Scalable Video Event Retrieval by Visual State Binary Embedding
Yu, L., Huang, Z. H., Cao, J., & Shen, H. T. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2557059 · Cited by 19 · see citing works
Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents
Yu, L., Yang, Y., Huang, Z. H., Wang, P., Song, J., & Shen, H. T. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2614136 · Cited by 19 · see citing works
Multi-Modal Knowledge Representation Learning via Webly-Supervised Relationships Mining
Nian, F., Bao, B., Li, T., & Xu, C. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123443 · Cited by 17 · see citing works
Background subtraction based on circulant matrix
Dou, J., Qin, Q., & Tu, Z. (2016) · Signal Image and Video Processing · doi:10.1007/s11760-016-0975-5 · Cited by 15 · see citing works
Audio-based Near-Duplicate Video Retrieval with Audio Similarity Learning
Avgoustinakis, P., Kordopatis-Zilos, G., Papadopoulos, S., Symeonidis, A. L., & Kompatsiaris, I. Y. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413056 · Cited by 15 · see citing works
Event retrieval using motion barcodes
Ben-Artzi, G., Werman, M., & Peleg, S. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351277 · Cited by 13 · see citing works
Soltanian, M. R. K., Amini, S., & Ghaemmaghami, S. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2959426 · Cited by 13 · see citing works
Object detection in video sequences by a temporal modular self-adaptive SOM
Ramírez-Alonso, G. M. D. J., & Chacon-Murguia, M. I. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1859-2 · Cited by 13 · see citing works
Saleh, A., Abdel‐Nasser, M., García, M. Á., & Puig, D. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.06.010 · Cited by 12 · see citing works
The Application of KAZE Features to the Classification Echocardiogram Videos
Li, W., Yu, Q., Loomes, M. J., & Gao, X. W. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24471-6_6 · Cited by 12 · see citing works
What Is Usual in Unusual Videos? Trajectory Snippet Histograms for Discovering Unusualness
İşcen, A., Armagan, A., & Duygulu, P. (2014) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2014.123 · 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
Self-citationVVS: 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
A Self-Selective Correlation Ship Tracking Method for Smart Ocean Systems
Xu, K., Song, B., Guo, J., Du, X., & Guizani, M. (2019) · Sensors · doi:10.3390/s19040821 · Cited by 11 · see citing works
Understanding and localizing activities from correspondences of clustered trajectories
Turchini, F., Seidenari, L., & Bimbo, A. D. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.11.007 · Cited by 11 · see citing works
Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds
Anirudh, R., & Turaga, P. K. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0835-8 · Cited by 11 · see citing works
Choudhary, A., & Chaudhury, S. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0321 · Cited by 11 · see citing works
Zhao, F., Huang, Y., Wang, L., Xiang, T., & Tan, T. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0896-3 · Cited by 10 · see citing works
Distribution Sensitive Product Quantization
Li, L., Hu, Q., Han, Y., & Li, X. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2759277 · 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
Vectors of locally aggregated centers for compact video representation
Abbas, A., Deligiannis, N., & Andreopoulos, Y. (2015) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2015.7177501 · Cited by 9 · see citing works
Exploiting spatial-temporal context for trajectory based action video retrieval
Zhang, L., Wang, Z., Yao, T., Staoh, S., Mei, T., & Feng, D. D. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4353-2 · Cited by 8 · see citing works
Image Semantic Representation for Event Understanding
Rodrigues, C. M., Pereira, L. A. M., Rocha, A. D. R., & Dias, Z. (2019) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs47025.2019.9035102 · Cited by 7 · see citing works
Efficient video-based retrieval of human motion with flexible alignment
Gupta, A., He, J., Martínez, J., Little, J. J., & Woodham, R. J. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477588 · Cited by 7 · see citing works
Fast Covariant VLAD for Image Search
Zhao, W., Ngo, C., & Wang, H. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2585023 · 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
Spatio-temporal feature based VLAD for efficient video retrieval
Reddy, M. K., Arora, S., & Babu, R. V. (2013) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2013.6776268 · Cited by 6 · see citing works
Understanding Sport Activities from Correspondences of Clustered Trajectories
Turchini, F., Seidenari, L., & Bimbo, A. D. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.103 · 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
Video alignment for phylogenetic analysis
Lameri, S., Bestagini, P., & Tubaro, S. (2016) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2016.7760650 · Cited by 5 · see citing works
Triple-Bit Quantization with Asymmetric Distance for Image Content Security
Xu, D., Xie, H., & Yan, C. C. (2017) · Machine Vision and Applications · doi:10.1007/s00138-017-0853-3 · 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
Self-citationBag of Genres for Video Retrieval
Duarte, L. A., Penatti, O. A. B., & Almeida, J. (2016) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2016.043 · Cited by 4 · 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
OSVC - Open Short Video Collection 1.0
Rossetto, L., Giangreco, I., & Schuldt, H. (2015) · Cited by 4 · see citing works
Gao, Z., Wang, L., Jojić, N., Niu, Z., Zheng, N., & Hua, G. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2866114 · Cited by 4 · see citing works
Learning Multi-scale Representations with Single-stream Network for Video Retrieval
Wang, C.-H., Tseng, Y., Chiang, T.-H., & Chen, Y.-A. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00656 · Cited by 4 · see citing works
Future-Supervised Retrieval of Unseen Queries for Live Video
Cappallo, S., & Snoek, C. G. M. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123437 · Cited by 4 · see citing works
Optimized high order product quantization for approximate nearest neighbors search
Li, L., & Hu, Q. (2019) · Frontiers of Computer Science · doi:10.1007/s11704-018-7049-5 · Cited by 4 · see citing works
A Consistency-Based Multimodal Graph Embedding Method for Dimensionality Reduction
Kalamaras, I., Drosou, A., Polychronidou, E., & Tzovaras, D. K. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.4 · Cited by 3 · see citing works
Mei, L., He, Y., Fishani, F. J., Yu, Y., Zhang, L., & Rhodin, H. (2023) · Remote Sensing · doi:10.3390/rs15040953 · Cited by 3 · see citing works
Large-Scale Video Event Detection
Ye, G. (2015) · doi:10.7916/d8bg2ngv · Cited by 3 · see citing works
Parameter-Efficient Deep Neural Networks With Bilinear Projections
Yu, L., Gao, Y., Zhou, J. W., & Zhang, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3016688 · 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
Cluster encoding for modelling temporal variation in video
Rostamzadeh, N., Uijlings, J., Mironica, I., Abadi, M. K., Ionescu, B. E., & Sebe, N. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351483 · Cited by 3 · see citing works
Bag of Attributes for Video Event Retrieval
Duarte, L. A., Penatti, O. A. B., & Almeida, J. (2018) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2018.00064 · Cited by 3 · see citing works
A Novel Time Series Kernel for Sequences Generated by LTI Systems
Presti, L. L., & Cascia, M. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54187-7_29 · Cited by 3 · see citing works
Video Alignment Using Bi-Directional Attention Flow in a Multi-Stage Learning Model
Abobeah, R., Shoukry, A., & Katto, J. (2020) · IEEE Access · doi:10.1109/access.2020.2967750 · Cited by 3 · see citing works
Event Matching from Significantly Different Views using Motion Barcodes.Preprint
Ben-Artzi, G., Werman, M., & Peleg, B. (2014) · 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
Variable scale and anti-occlusion object tracking method with multiple feature integration
Li, K., Liu, Y., & Li, N. (2016) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2016.7883101 · Cited by 2 · see citing works
Maze: A Cost-Efficient Video Deduplication System at Web-scale
Qin, A., Xiao, M., Huang, B., & Zhang, X. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548145 · Cited by 2 · see citing works
An Interactive System for Multimedia Retrieval in Video Collection with Temporal Integration
Gia, K. P., Le, H. B. T., Huynh, P. L. N., Tran, S. P. L., Hoang, L., Xuan, T. P., Ham, D. T., Huynh, T., & Hoàng, K. V. (2023) · International Symposium on Information and Communication Technology · doi:10.1145/3628797.3629019 · Cited by 2 · see citing works
Joint Semantic Graph and Visual Image Retrieval Guided Video Copy Detection
Zhang, Z., Mao, X. S., Zhang, J., Lian, W., Xu, S., & Zhang, X. (2023) · International Conference on Communication and Information Processing (ICCIP) · doi:10.1145/3638884.3638896 · Cited by 2 · see citing works
van der Kreeft, P., Macquarrie, K., Kemman, M., Kleppe, M., & McGuinness, K. (2014) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2014.6849852 · Cited by 2 · see citing works
Video action re-localization using spatio-temporal correlation
Ramaswamy, A., Seemakurthy, K., Gubbi, J., & Balamuralidhar, P. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00025 · Cited by 2 · see citing works
Collaborative Correlation Filter Tracking with Online Re-detection
Song, Z., Sun, J., & Duan, B. (2019) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec.2019.8728975 · Cited by 2 · see citing works
Rajalingam, M. (2020) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-53047-1_4 · 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
Block based video alignment with linear time and space complexity
Kappeler, A., Iliadis, M., Wang, H., & Katsaggelos, A. K. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532975 · Cited by 1 · see citing works
- Authors
- Revaud, J., Douze, M., Schmid, C., & Jegou, H.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2459-2466
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2165502232
- Cited by
- 133
- References
- 27
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 5 · 2023: 12 · 2022: 9 · 2021: 7 · 2020: 3 · 2019: 13 · 2018: 5 · 2017: 15 · 2016: 18 · 2015: 21 · 2014: 15 · 2013: 4
- FWCI
- 12.34
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- event retrieval, circulant matrices, product quantization, video copy detection, TRECVID
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.
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