A supervised approach to movie emotion tracking

Malandrakis, N; Potamianos, A; Evangelopoulos, G; Zlatintsi, A

dc.contributor.author	Malandrakis, N	en
dc.contributor.author	Potamianos, A	en
dc.contributor.author	Evangelopoulos, G	en
dc.contributor.author	Zlatintsi, A	en
dc.date.accessioned	2014-03-01T02:47:15Z
dc.date.available	2014-03-01T02:47:15Z
dc.date.issued	2011	en
dc.identifier.issn	15206149	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33035
dc.subject	Emotion recognition	en
dc.subject	Machine learning	en
dc.subject	Multimedia databases	en
dc.subject	Psychological emotion dimensions	en
dc.subject.other	Affective response	en
dc.subject.other	Annotated database	en
dc.subject.other	Audio-visual features	en
dc.subject.other	Content recognition	en
dc.subject.other	Continuous scale	en
dc.subject.other	Continuous time	en
dc.subject.other	Continuous value	en
dc.subject.other	Emotion recognition	en
dc.subject.other	Feature sets	en
dc.subject.other	Machine-learning	en
dc.subject.other	Macroscopic levels	en
dc.subject.other	Multimedia database	en
dc.subject.other	Psychological emotion dimensions	en
dc.subject.other	Spline interpolation	en
dc.subject.other	Supervised algorithm	en
dc.subject.other	Supervised learning methods	en
dc.subject.other	Video frame	en
dc.subject.other	Continuous time systems	en
dc.subject.other	Database systems	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Interpolation	en
dc.subject.other	Learning systems	en
dc.subject.other	Signal processing	en
dc.subject.other	Speech communication	en
dc.subject.other	Speech recognition	en
dc.subject.other	Motion pictures	en
dc.title	A supervised approach to movie emotion tracking	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICASSP.2011.5946961	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICASSP.2011.5946961	en
heal.identifier.secondary	5946961	en
heal.publicationDate	2011	en
heal.abstract	In this paper, we present experiments on continuous time, continuous scale affective movie content recognition (emotion tracking). A major obstacle for emotion research has been the lack of appropriately annotated databases, limiting the potential for supervised algorithms. To that end we develop and present a database of movie affect, annotated in continuous time, on a continuous valence-arousal scale. Supervised learning methods are proposed to model the continuous affective response using hidden Markov Models (independent) in each dimension. These models classify each video frame into one of seven discrete categories (in each dimension); the discrete-valued curves are then converted to continuous values via spline interpolation. A variety of audio-visual features are investigated and an optimal feature set is selected. The potential of the method is experimentally verified on twelve 30-minute movie clips with good precision at a macroscopic level. © 2011 IEEE.	en
heal.journalName	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en
dc.identifier.doi	10.1109/ICASSP.2011.5946961	en
dc.identifier.spage	2376	en
dc.identifier.epage	2379	en