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]
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“clude CC_WEB_VIDEO [49], UQ_VIDEO [42], MUSCLEVCD [30], TRECVID-CBCD 2011 [29], VCDB [21] and EVVE [35].”
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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
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
Event Matching from Significantly Different Views using Motion Barcodes.Preprint
Ben-Artzi, G., Werman, M., & Peleg, B. (2014) · arXiv (Cornell University) · Cited by 3
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
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
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
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
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
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
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
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
Rajalingam, M. (2020) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-53047-1_4 · Cited by 2
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
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
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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