From crowdsourced rankings to affective ratings

Yoann Baveye 1 Emmanuel Dellandréa 1 Christel Chamaret 2 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 Technicolor R & I [Cesson Sévigné]
Abstract : Automatic prediction of emotions requires reliably annotated data which can be achieved using scoring or pairwise ranking. But can we predict an emotional score using a ranking-based annotation approach? In this paper, we propose to answer this question by describing a regression analysis to map crowdsourced rankings into affective scores in the induced valence- arousal emotional space. This process takes advantages of the Gaussian Processes for regression that can take into account the variance of the ratings and thus the subjectivity of emotions. Regression models successfully learn to fit input data and provide valid predictions. Two distinct experiments were realized using a small subset of the publicly available LIRIS-ACCEDE affective video database for which crowdsourced ranks, as well as affective ratings, are available for arousal and valence. It allows to enrich LIRIS-ACCEDE by providing absolute video ratings for the whole database in addition to video rankings that are already available.
Type de document :
Communication dans un congrès
2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Jul 2014, Chengdu, China. pp.1-6, 2014, 〈10.1109/ICMEW.2014.6890568〉
Liste complète des métadonnées
https://hal.archives-ouvertes.fr/hal-01313166
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : lundi 9 mai 2016 - 16:11:19
Dernière modification le : mercredi 31 octobre 2018 - 12:24:25

Identifiants

Collections

EC-LYON | LIRIS | INSA-LYON | UNIV-LYON1

Citation

Yoann Baveye, Emmanuel Dellandréa, Christel Chamaret, Liming Chen. From crowdsourced rankings to affective ratings. 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Jul 2014, Chengdu, China. pp.1-6, 2014, 〈10.1109/ICMEW.2014.6890568〉. 〈hal-01313166〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

124

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD