Cited by 133Advanced Image and Video Retrieval TechniquesVideo Analysis and SummarizationImage Retrieval and Classification Techniques

Event Retrieval in Large Video Collections with Circulant Temporal Encoding

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Revaud, Jerome, Douze, Matthijs, Schmid, Cordelia, Jegou, Herve·2013·2013 IEEE Conference on Computer Vision and Pattern Recognition

Abstract

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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Registry

Crossref · 10.1109/cvpr.2013.318

Cites

24 of 24 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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.”
  • 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.”
  • 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]

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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
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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