Feature Selection and Multimodal Fusion for Estimating Emotions Evoked by Movie Clips

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dc.authorid0000-0001-6342-428X
dc.authorid0000-0001-7947-5508
dc.authorwosidSalah, Albert Ali/ABH-5561-2020
dc.contributor.authorTimar, Yasemin
dc.contributor.authorKarslıoğlu, Nihan
dc.contributor.authorKaya, Heysem
dc.contributor.authorSalah, Albert Ali
dc.date.accessioned2022-05-11T14:15:53Z
dc.date.available2022-05-11T14:15:53Z
dc.date.issued2018
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description8th ACM International Conference on Multimedia Retrieval (ACM ICMR) -- JUN 11-14, 2018 -- Yokohama, JAPAN
dc.description.abstractPerceptual understanding of media content has many applications, including content-based retrieval, marketing, content optimization, psychological assessment, and affect-based learning. In this paper, we model audio visual features extracted from videos via machine learning approaches to estimate the affective responses of the viewers. We use the LIRIS-ACCEDE dataset and the MediaEval 2017 Challenge setting to evaluate the proposed methods. This dataset is composed of movies of professional or amateur origin, annotated with viewers' arousal, valence, and fear scores. We extract a number of audio features, such as Mel-frequency Cepstral Coefficients, and visual features, such as dense SIFT, hue-saturation histogram, and features from a deep neural network trained for object recognition. We contrast two different approaches in the paper, and report experiments with different fusion and smoothing strategies. We demonstrate the benefit of feature selection and multimodal fusion on estimating affective responses to movie segments.
dc.description.sponsorshipAssoc Comp Machinery, ACM SIGMM
dc.identifier.doi10.1145/3206025.3206074
dc.identifier.endpage412
dc.identifier.isbn978-1-4503-5046-4
dc.identifier.startpage405
dc.identifier.urihttps://doi.org/10.1145/3206025.3206074
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6108
dc.identifier.wosWOS:000461145900055
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKaya, Heysem
dc.language.isoen
dc.publisherAssoc Computing Machinery
dc.relation.ispartofIcmr '18: Proceedings of the 2018 Acm International Conference on Multimedia Retrieval
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAffective computing
dc.subjectmultimodal interaction
dc.subjectemotion estimation
dc.subjectaudio-visual features
dc.subjectmovie analysis
dc.subjectface analysis
dc.subjectExtreme Learning-Machine
dc.titleFeature Selection and Multimodal Fusion for Estimating Emotions Evoked by Movie Clips
dc.typeConference Object

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