Mediaeval 2017 predicting media interestingness task

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

Mediaeval 2017 predicting media interestingness task

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

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

20 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 language	English
Title of host publication	Multimedia Benchmark Workshop
Subtitle of host publication	Working 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
Publisher	CEUR
Number of pages	3
Publication status	Published - 2017
MoE publication type	A4 Conference publication
Event	Multimedia Benchmark Workshop - Dublin, Ireland Duration: 13 Sept 2017 → 15 Sept 2017

Publication series

Name	CEUR Workshop Proceedings
Publisher	Rheinisch-Westfaelische Technische Hochschule Aachen
Volume	1984
ISSN (Electronic)	1613-0073

Workshop

Workshop	Multimedia Benchmark Workshop
Country/Territory	Ireland
City	Dublin
Period	13/09/2017 → 15/09/2017

Access to Document

http://ceur-ws.org/Vol-1984/

Cite this

Demarty, C. H., Sjöberg, M., Ionescu, B., Do, T. T., Gygli, M., & Duong, N. Q. K. (2017). Mediaeval 2017 predicting media interestingness task. In Multimedia Benchmark Workshop: Working 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 (CEUR Workshop Proceedings; Vol. 1984). CEUR. http://ceur-ws.org/Vol-1984/

@inproceedings{c6c10e1e8df04c0c9602cc116152ecc6,

title = "Mediaeval 2017 predicting media interestingness task",

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.",

author = "Demarty, {Claire Helene} and Mats Sj{\"o}berg and Bogdan Ionescu and Do, {Thanh Toan} and Michael Gygli and Duong, {Ngoc Q.K.}",

year = "2017",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR",

booktitle = "Multimedia Benchmark Workshop",

address = "Germany",

note = "Multimedia Benchmark Workshop ; Conference date: 13-09-2017 Through 15-09-2017",

}

Demarty, CH, Sjöberg, M, Ionescu, B, Do, TT, Gygli, M & Duong, NQK 2017, Mediaeval 2017 predicting media interestingness task. in Multimedia Benchmark Workshop: Working 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. CEUR Workshop Proceedings, vol. 1984, CEUR, Multimedia Benchmark Workshop, Dublin, Ireland, 13/09/2017. <http://ceur-ws.org/Vol-1984/>

Mediaeval 2017 predicting media interestingness task. / Demarty, Claire Helene; Sjöberg, Mats; Ionescu, Bogdan et al.
Multimedia Benchmark Workshop: Working 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. CEUR, 2017. (CEUR Workshop Proceedings; Vol. 1984).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Mediaeval 2017 predicting media interestingness task

AU - Demarty, Claire Helene

AU - Sjöberg, Mats

AU - Ionescu, Bogdan

AU - Do, Thanh Toan

AU - Gygli, Michael

AU - Duong, Ngoc Q.K.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

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M3 - Conference article in proceedings

AN - SCOPUS:85035029243

T3 - CEUR Workshop Proceedings

BT - Multimedia Benchmark Workshop

PB - CEUR

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Y2 - 13 September 2017 through 15 September 2017

ER -

Mediaeval 2017 predicting media interestingness task

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