Towards learning emotional subspace

Tobey H. Ko, Zhonglei Gu, Tiantian He, Yang Liu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

We introduce a model designed to predict emotional impact of movies through affective video content analysis. Specifically, our approach utilizes a two-stage learning framework, which first conducts subspace learning using emotion preserving embedding (EPE) or biased discriminant embedding (BDE) to uncover the informative subspace from the original feature space according to the continuous or discrete emotional labels, respectively, and then carries out the prediction utilizing the support vector machine (SVM). Experimentation on a movie dataset validates the effectiveness of our learning framework. Copyright held by the owner/author(s).

Original languageEnglish
Title of host publicationWorking Notes Proceedings of the MediaEval 2018 Workshop
EditorsMartha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones
PublisherCEUR-WS
Number of pages3
Publication statusPublished - Oct 2018
EventMediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018
https://ceur-ws.org/Vol-2283/

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume2283
ISSN (Print)1613-0073

Conference

ConferenceMediaEval 2018: Multimedia Benchmark Workshop
Country/TerritoryFrance
CitySophia Antipolis
Period29/10/1831/10/18
Internet address

Scopus Subject Areas

  • Computer Science(all)

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