Kiziltepe, Rukiye Savran and Sweeney, Lorin and Constantin, Mihai Gabriel and Doctor, Faiyaz and García Seco de Herrera, Alba and Demarty, Claire-Héléne and Healy, Graham and Ionescu, Bogdan and Smeaton, Alan F (2021) An annotated video dataset for computing video memorability. Data in Brief, 39. p. 107671. DOI https://doi.org/10.1016/j.dib.2021.107671
Kiziltepe, Rukiye Savran and Sweeney, Lorin and Constantin, Mihai Gabriel and Doctor, Faiyaz and García Seco de Herrera, Alba and Demarty, Claire-Héléne and Healy, Graham and Ionescu, Bogdan and Smeaton, Alan F (2021) An annotated video dataset for computing video memorability. Data in Brief, 39. p. 107671. DOI https://doi.org/10.1016/j.dib.2021.107671
Kiziltepe, Rukiye Savran and Sweeney, Lorin and Constantin, Mihai Gabriel and Doctor, Faiyaz and García Seco de Herrera, Alba and Demarty, Claire-Héléne and Healy, Graham and Ionescu, Bogdan and Smeaton, Alan F (2021) An annotated video dataset for computing video memorability. Data in Brief, 39. p. 107671. DOI https://doi.org/10.1016/j.dib.2021.107671
Abstract
Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 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.
Item Type: | Article |
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Uncontrolled Keywords: | Video memorability; Machine learning; Human memory; Mediaeval benchmark |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Jan 2022 10:42 |
Last Modified: | 30 Oct 2024 21:05 |
URI: | http://repository.essex.ac.uk/id/eprint/32070 |
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