Abstract
In this paper, the Predicting Media Interestingness task which is running for the second year as part of the MediaEval 2017 Benchmarking Initiative for Multimedia Evaluation, is presented. For the task, participants are expected to create systems that automatically select images and video segments that are considered to be the most interesting for a common viewer. All task characteristics are described, namely the task use case and challenges, the released data set and ground truth, the required participant runs and the evaluation metrics.
| Original language | English |
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| Title of host publication | Working Notes Proceedings of the MediaEval 2017 Workshop |
| Editors | Guillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J.F. Jones, Martha Larson |
| Place of Publication | Aachen Germany |
| Publisher | CEUR-WS |
| Number of pages | 3 |
| Publication status | Published - 2017 |
| Externally published | Yes |
| Event | Multimedia Benchmark Workshop 2017 - Dublin, Ireland Duration: 13 Sep 2017 → 15 Sep 2017 http://ceur-ws.org/Vol-1984/ (Proceedings) |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR-WS.org |
| Volume | 1984 |
| ISSN (Print) | 1613-0073 |
Conference
| Conference | Multimedia Benchmark Workshop 2017 |
|---|---|
| Abbreviated title | MediaEval 2017 |
| Country/Territory | Ireland |
| City | Dublin |
| Period | 13/09/17 → 15/09/17 |
| Internet address |
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