Work

MediaEval 2016 Predicting Media Interestingness Task

Year: 2016

Type: conference-paper

Abstract: 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.

Source: Työväentutkimus Vuosikirja

Authors: Demarty, Claire-Hélène, Sjöberg, Mats Viktor, Ionescu, Bogdan, Do, Thanh-Toan, Wang, Hanli, Duong, Ngoc Q. K., Lefèbvre, Frédéric

Institutions: Technicolor (France), Technicolor (United States), University of Helsinki, Helsinki Institute for Information Technology, Universitatea Națională de Știință și Tehnologie Politehnica București, Singapore University of Technology and Design, Tongji University

Language: English

Topic: Video Analysis and Summarization

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Fudan University

OpenAlex ID: W2573570971

FWCI: 1.9

Cites: 20

Cited by: 24

Related to: 10