publication . Article . Preprint . Other literature type . 2019

FIVR: Fine-Grained Incident Video Retrieval

Kordopatis-Zilos, Giorgos; Papadopoulos, Symeon; Patras, Ioannis; Kompatsiaris, Ioannis;
Open Access English
  • Published: 18 Mar 2019 Journal: IEEE Transactions on Multimedia, volume 21, issue 10, pages 2,638-2,652 (issn: 1520-9210, eissn: 1941-0077, Copyright policy)
Abstract
This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). Given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikip...
Persistent Identifiers
Subjects
free text keywords: Fine-grained Incident Video Retrieval, near-duplicate videos, video retrieval, Computer Science - Multimedia, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Media Technology, Signal Processing, Electrical and Electronic Engineering, Computer Science Applications, Task analysis, Information retrieval, Annotation, Encyclopedia, Computer science, Benchmarking, Benchmark (computing), Electronic publishing, business.industry, business, The Internet
Funded by
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
,
EC| WeVerify
Project
WeVerify
WIDER AND ENHANCED VERIFICATION FOR YOU
  • Funder: European Commission (EC)
  • Project Code: 825297
  • Funding stream: H2020 | IA
Validated by funder
41 references, page 1 of 3

[1] “TREC Video Retrieval Evaluation: TRECVID,” 2018. [Online]. Available: https://trecvid.nist.gov/

[2] M. Abadi, A. Agarwal, P. Barham, and et al., “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

[3] A. Basharat, Y. Zhai, and M. Shah, “Content based video matching using spatiotemporal volumes,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 360-377, 2008.

[4] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” in European conference on computer vision. Springer, 2006, pp. 404-417.

[5] S. Bird and E. Loper, “Nltk: the natural language toolkit,” in Proceedings of the ACL 2004 on Interactive poster and demonstration sessions. Association for Computational Linguistics, 2004, p. 31.

[6] M. Brown, “Reporting on Las Vegas, Pixel by Pixel,” 2017. [Online]. Available: https://www.nytimes.com/2017/10/23/insider/ reporting-on-las-vegas-pixel-by-pixel.html

[7] Y. Cai, L. Yang, W. Ping, F. Wang, T. Mei, X.-S. Hua, and S. Li, “Million-scale near-duplicate video retrieval system,” in Proceedings of the 19th ACM international conference on Multimedia. ACM, 2011, pp. 837-838.

[8] C.-L. Chou, H.-T. Chen, and S.-Y. Lee, “Pattern-based near-duplicate video retrieval and localization on web-scale videos,” IEEE Transactions on Multimedia, vol. 17, no. 3, pp. 382-395, 2015.

[9] M. Douze, H. Je´gou, and C. Schmid, “An image-based approach to video copy detection with spatio-temporal post-filtering,” IEEE Transactions on Multimedia, vol. 12, no. 4, pp. 257-266, 2010. [OpenAIRE]

[10] L. Gao, P. Wang, J. Song, Z. Huang, J. Shao, and H. T. Shen, “Event video mashup: From hundreds of videos to minutes of skeleton.” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, 2017, pp. 1323-1330.

[11] A. Hanjalic, R. L. Lagendijk, and J. Biemond, “Automated highlevel movie segmentation for advanced video-retrieval systems,” IEEE transactions on circuits and systems for video technology, vol. 9, no. 4, pp. 580-588, 1999.

[12] Y. Hao, T. Mu, R. Hong, M. Wang, N. An, and J. Y. Goulermas, “Stochastic multiview hashing for large-scale near-duplicate video retrieval,” IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 1-14, 2017.

[13] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.

[14] J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, and R. Zabih, “Image indexing using color correlograms,” in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. IEEE, 1997, pp. 762-768.

[15] Itseez, “Open source computer vision library,” https://github.com/itseez/ opencv, 2018.

41 references, page 1 of 3
Abstract
This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). Given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikip...
Persistent Identifiers
Subjects
free text keywords: Fine-grained Incident Video Retrieval, near-duplicate videos, video retrieval, Computer Science - Multimedia, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Information Retrieval, Media Technology, Signal Processing, Electrical and Electronic Engineering, Computer Science Applications, Task analysis, Information retrieval, Annotation, Encyclopedia, Computer science, Benchmarking, Benchmark (computing), Electronic publishing, business.industry, business, The Internet
Funded by
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
,
EC| WeVerify
Project
WeVerify
WIDER AND ENHANCED VERIFICATION FOR YOU
  • Funder: European Commission (EC)
  • Project Code: 825297
  • Funding stream: H2020 | IA
Validated by funder
41 references, page 1 of 3

[1] “TREC Video Retrieval Evaluation: TRECVID,” 2018. [Online]. Available: https://trecvid.nist.gov/

[2] M. Abadi, A. Agarwal, P. Barham, and et al., “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

[3] A. Basharat, Y. Zhai, and M. Shah, “Content based video matching using spatiotemporal volumes,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 360-377, 2008.

[4] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” in European conference on computer vision. Springer, 2006, pp. 404-417.

[5] S. Bird and E. Loper, “Nltk: the natural language toolkit,” in Proceedings of the ACL 2004 on Interactive poster and demonstration sessions. Association for Computational Linguistics, 2004, p. 31.

[6] M. Brown, “Reporting on Las Vegas, Pixel by Pixel,” 2017. [Online]. Available: https://www.nytimes.com/2017/10/23/insider/ reporting-on-las-vegas-pixel-by-pixel.html

[7] Y. Cai, L. Yang, W. Ping, F. Wang, T. Mei, X.-S. Hua, and S. Li, “Million-scale near-duplicate video retrieval system,” in Proceedings of the 19th ACM international conference on Multimedia. ACM, 2011, pp. 837-838.

[8] C.-L. Chou, H.-T. Chen, and S.-Y. Lee, “Pattern-based near-duplicate video retrieval and localization on web-scale videos,” IEEE Transactions on Multimedia, vol. 17, no. 3, pp. 382-395, 2015.

[9] M. Douze, H. Je´gou, and C. Schmid, “An image-based approach to video copy detection with spatio-temporal post-filtering,” IEEE Transactions on Multimedia, vol. 12, no. 4, pp. 257-266, 2010. [OpenAIRE]

[10] L. Gao, P. Wang, J. Song, Z. Huang, J. Shao, and H. T. Shen, “Event video mashup: From hundreds of videos to minutes of skeleton.” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017. AAAI press, 2017, pp. 1323-1330.

[11] A. Hanjalic, R. L. Lagendijk, and J. Biemond, “Automated highlevel movie segmentation for advanced video-retrieval systems,” IEEE transactions on circuits and systems for video technology, vol. 9, no. 4, pp. 580-588, 1999.

[12] Y. Hao, T. Mu, R. Hong, M. Wang, N. An, and J. Y. Goulermas, “Stochastic multiview hashing for large-scale near-duplicate video retrieval,” IEEE Transactions on Multimedia, vol. 19, no. 1, pp. 1-14, 2017.

[13] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.

[14] J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, and R. Zabih, “Image indexing using color correlograms,” in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. IEEE, 1997, pp. 762-768.

[15] Itseez, “Open source computer vision library,” https://github.com/itseez/ opencv, 2018.

41 references, page 1 of 3
Any information missing or wrong?Report an Issue