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 contributionScientificpeer-review

12 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 publicationMultimedia Benchmark Workshop
Subtitle of host publicationWorking Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017), Dublin, Ireland, September 13-15, 2017
PublisherCEUR
Number of pages3
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventMultimedia Benchmark Workshop - Dublin, Ireland
Duration: 13 Sep 201715 Sep 2017

Publication series

NameCEUR Workshop Proceedings
PublisherRheinisch-Westfaelische Technische Hochschule Aachen
Volume1984
ISSN (Electronic)1613-0073

Workshop

WorkshopMultimedia Benchmark Workshop
CountryIreland
CityDublin
Period13/09/201715/09/2017

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