The DOI matches its Crossref record; 24 of the 27 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
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
A survey of image registration techniques
Brown, L. G. (1992) · ACM Computing Surveys · doi:10.1145/146370.146374 · Cited by 4,079
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
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×
“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
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
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
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
Evaluation campaigns and TRECVid
Smeaton, A. F., Over, P., & Kraaij, W. (2006) · doi:10.1145/1178677.1178722 · Cited by 1,227
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
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
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
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
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
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
Correlation pattern recognition
(2006) · Choice Reviews Online · doi:10.5860/choice.44-1544 · Cited by 255
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
\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
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 137
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
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
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
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 27
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
Research works printed but not linked: 1
- nowak, 2006, Sampling strategies for bag-offeatures image classification, ECCV
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
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
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 311
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 283
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
Metadata
- 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
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