Abstract
With the widespread use of smartphones as recording devices and the massive growth in bandwidth, the number and volume of video collections has increased significantly in the last years. This poses novel challenges to the management of these large-scale video data and especially to the analysis of and retrieval from such video collections. At the same time, existing video datasets used for research and experimentation are either not large enough to represent current collections or do not reflect the properties of video commonly found on the Internet in terms of content, length, or resolution.
In this paper, we introduce the Vimeo Creative Commons Collection, in short V3C, a collection of 28’450 videos (with overall length of about 3’800 h) published under creative commons license on Vimeo. V3C comes with a shot segmentation for each video, together with the resulting keyframes in original as well as reduced resolution and additional metadata. It is intended to be used from 2019 at the International large-scale TREC Video Retrieval Evaluation campaign (TRECVid).
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References
Abu-El-Haija, S., et al.: Youtube-8m: a large-scale video classification benchmark. arXiv preprint arXiv:1609.08675 (2016)
Awad, G., et al.: Trecvid 2017: evaluating ad-hoc and instance video search, events detection, video captioning and hyperlinking. In: Proceedings of TRECVID 2017. NIST, USA (2017)
Cobârzan, C., et al.: Interactive video search tools: a detailed analysis of the video browser showdown 2015. Multimed. Tools Appl. 76(4), 5539–5571 (2017)
Cohendet, R., Yadati, K., Duong, N.Q.K., Demarty, C.-H.: Annotating, understanding, and predicting long-term video memorability. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 178–186. ACM (2018)
Over, P., Awad, G., Smeaton, A.F., Foley, C., Lanagan, J.: Creating a web-scale video collection for research. In: Proceedings of the 1st Workshop on Web-Scale Multimedia Corpus, pp. 25–32. ACM (2009)
Rossetto, L., Giangreco, I., Schuldt, H.: Cineast: a multi-feature sketch-based video retrieval engine. In: 2014 IEEE International Symposium on Multimedia (ISM), pp. 18–23. IEEE (2014)
Rossetto, L., Schuldt, H.: Web video in numbers - an analysis of web-video metadata. arXiv preprint arXiv:1707.01340 (2017)
Thomee, B., et al.: YFCC100M: the new data in multimedia research. Commun. ACM 59(2), 64–73 (2016)
YouTube Terms of Service. https://www.youtube.com/static?template=terms (2018). Accessed 15 June 2018
Acknowledgements
This work was partly supported by the Swiss National Science Foundation, project IMOTION (20CH21_151571).
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Rossetto, L., Schuldt, H., Awad, G., Butt, A.A. (2019). V3C – A Research Video Collection. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_29
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DOI: https://doi.org/10.1007/978-3-030-05710-7_29
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