MediaEval 2016 predicting media interestingness task

Research output: Contribution to journalConference articleScientificpeer-review

Researchers

  • Claire Hélène Demarty
  • Mats Sjöberg

  • Bogdan Ionescu
  • Thanh Toan Do
  • Hanli Wang
  • Ngoc Q.K. Duong
  • Frédéric Lefebvre

Research units

  • Technicolor
  • University of Helsinki
  • University Politehnica of Bucharest
  • Singapore University of Technology and Design
  • University of Science
  • Tongji University

Abstract

This paper provides an overview of the Predicting Media Interestingness task that is organized as part of the Media-Eval 2016 Benchmarking Initiative for Multimedia Evaluation. The task, which is running for the first year, expects participants to create systems that automatically select images and video segments that are considered to be the most interesting for a common viewer. In this paper, we present the task use case and challenges, the proposed data set and ground truth, the required participant runs and the evaluation metrics.

Details

Original languageEnglish
Number of pages3
JournalCEUR Workshop Proceedings
Volume1739
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication

ID: 17208897