DOI

Memorability can be regarded as a useful metric of video importance to help make a choice between competing videos. Research on computational understanding of video memorability is however in its early stages. There is no available dataset for modelling purposes, and the few previous attempts provided protocols to collect video memorability data that would be difficult to generalize. Furthermore, the computational features needed to build a robust memorability predictor remain largely undiscovered. In this article, we propose a new protocol to collect long-term video memorability annotations. We measure the memory performances of 104 participants from weeks to years after memorization to build a dataset of 660 videos for video memorability prediction. This dataset is made available for the research community. We then analyze the collected data in order to better understand video memorability, in particular the effects of response time, duration of memory retention and repetition of visualization on video memorability. We finally investigate the use of various types of audio and visual features and build a computational model for video memorability prediction. We conclude that high level visual semantics help better predict the memorability of videos.

Original languageEnglish
Title of host publicationICMR 2018 - Proceedings of the 2018 ACM International Conference on Multimedia Retrieval
EditorsKiyoharu Aizawa, Michael Lew , Shin'ichi Satoh
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages178-186
Number of pages9
ISBN (Print)978-1-4503-5046-4
DOIs
Publication statusPublished - 2018
Event8th ACM International Conference on Multimedia Retrieval, ICMR 2018 - Yokohama, Japan
Duration: 11 Jun 201814 Jun 2018

Conference

Conference8th ACM International Conference on Multimedia Retrieval, ICMR 2018
CountryJapan
CityYokohama
Period11/06/1814/06/18

    Research areas

  • Attributes, Global video features, Long-term memory, Measurement protocol, Scene understanding, Video memorability

ID: 53912653