MediaEval 2017 Predicting Media Interestingness Task
Overview & Synopsis
In this paper, the Predicting Media Interestingness task which is running for the second year as part of the MediaEval 2017 Bench-marking 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.
Indexed from repository: HAL Open Science
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hal.science)
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Document Information
| Document Title | MediaEval 2017 Predicting Media Interestingness Task |
|---|---|
| Author / Organization | Claire-Hélène Demarty, Mats Sjöberg, Bogdan Ionescu, Thanh-Toan Do, Michael Gygli, Ngoc Q K Duong |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2017 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-208153 |
| Original Source Link | Visit Source Page on hal.science » |