Towards learning emotional subspace

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

Research output: Contribution to journalConference articlepeer-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
JournalCEUR Workshop Proceedings
Volume2283
Publication statusPublished - 2018
Event2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018 - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018

Scopus Subject Areas

  • Computer Science(all)

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