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

11 Citations (Scopus)

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 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
PublisherCEUR-WS
Number of pages3
Publication statusPublished - 2017
Externally publishedYes
EventMultimedia Benchmark Workshop 2017 - Dublin, Ireland
Duration: 13 Sep 201715 Sep 2017
http://ceur-ws.org/Vol-1984/ (Proceedings)

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS.org
Volume1984
ISSN (Print)1613-0073

Conference

ConferenceMultimedia Benchmark Workshop 2017
Abbreviated titleMediaEval 2017
CountryIreland
CityDublin
Period13/09/1715/09/17
Internet address

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