Mediaeval 2017 predicting media interestingness task

Claire-Helene Demarty, Mats Sjöberg, Bogdan Ionescu, Thanh-Toan Do, Michael Gygli, Ngoc Q.K. Duong

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

19 Citations (Scopus)


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 languageEnglish
Title of host publicationWorking Notes Proceedings of the MediaEval 2017 Workshop
EditorsGuillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J.F. Jones, Martha Larson
Place of PublicationAachen Germany
Number of pages3
Publication statusPublished - 2017
Externally publishedYes
EventMultimedia Benchmark Workshop 2017 - Dublin, Ireland
Duration: 13 Sept 201715 Sept 2017 (Proceedings)

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


ConferenceMultimedia Benchmark Workshop 2017
Abbreviated titleMediaEval 2017
Internet address

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