Kiziltepe, Rukiye Savran, Sweeney, Lorin ORCID: 0000-0002-3427-1250, Constantin, Mihai Gabriel ORCID: 0000-0002-2312-6672, Doctor, Faiyaz ORCID: 0000-0002-8412-5489, García Seco de Herrera, Alba ORCID: 0000-0002-6509-5325, Demarty, Claire-Hélène, Healy, Graham ORCID: 0000-0001-6429-6339, Ionescu, Bogdan and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 (2021) An annotated video dataset for computing video memorability. Data In Brief, 39 . ISSN 2352-3409
Abstract
Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant’s ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Additional Information: | Article number: 107671 |
Uncontrolled Keywords: | Video memorability; Machine learning; Human memory; MediaEval Benchmark |
Subjects: | Computer Science > Artificial intelligence Computer Science > Image processing Computer Science > Machine learning Computer Science > Multimedia systems Computer Science > Digital video |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing Research Initiatives and Centres > INSIGHT Centre for Data Analytics |
Publisher: | Elsevier |
Official URL: | https://doi.org/10.1016/j.dib.2021.107671 |
Copyright Information: | © 2021 The Authors. Open Access (CC-BY-4.0) |
Funders: | AI4Media, A European Excellence Centre for Media, Society and Democracy, H2020 ICT-48-2020, grant #951911, Science Foundation Ireland SFI/12/RC/2289_P2, Turkish Ministry of National Education, National Institute of Standards and Technology (NIST) No. 60NANB19D155 |
ID Code: | 26516 |
Deposited On: | 09 Dec 2021 12:48 by Alan Smeaton . Last Modified 10 Dec 2021 13:54 |
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