LIRIS-ACCEDE: A Video Database for Affective Content Analysis - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Affective Computing Year : 2015

LIRIS-ACCEDE: A Video Database for Affective Content Analysis

Abstract

Research in affective computing requires ground truth data for training and benchmarking computational models for machine-based emotion understanding. In this paper, we propose a large video database, namely LIRIS-ACCEDE, for affective content analysis and related applications, including video indexing, summarization or browsing. In contrast to existing datasets with very few video resources and limited accessibility due to copyright constraints, LIRIS-ACCEDE consists of 9,800 good quality video excerpts with a large content diversity. All excerpts are shared under creative commons licenses and can thus be freely distributed without copyright issues. Affective annotations were achieved using crowdsourcing through a pair-wise video comparison protocol, thereby ensuring that annotations are fully consistent, as testified by a high inter-annotator agreement, despite the large diversity of raters' cultural backgrounds. In addition, to enable fair comparison and landmark progresses of future affective computational models, we further provide four experimental protocols and a baseline for prediction of emotions using a large set of both visual and audio features. The dataset (the video clips, annotations, features and protocols) is publicly available at: http://liris-accede.ec-lyon.fr/.
Fichier principal
Vignette du fichier
Liris-7059.pdf (1.9 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01375518 , version 1 (29-03-2017)

Identifiers

Cite

Yoann Baveye, Emmanuel Dellandréa, Christel Chamaret, Liming Chen. LIRIS-ACCEDE: A Video Database for Affective Content Analysis. IEEE Transactions on Affective Computing, 2015, 6 (1), pp.43-55. ⟨10.1109/TAFFC.2015.2396531⟩. ⟨hal-01375518⟩
824 View
1454 Download

Altmetric

Share

Gmail Facebook X LinkedIn More