HKBU at MediaEval 2017 Emotional impact of movies task

Yang Liu, Zhonglei Gu, Tobey H. Ko

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

1 Citation (Scopus)

Abstract

In this paper, we describe our model designed for automatic prediction of emotional impact of movies. Specifically, a two-stage learning framework is proposed. First, the dimensionality reduction techniques are employed to discover the key emotion information embedded in the original feature space. Specifically, we use a classical method principal component analysis (PCA) and a new algorithm biased discriminant embedding (BDE) to learn the subspace. After dimensionality reduction, SVM is utilized for prediction. Experimental results validate the effectiveness of our approaches.

Original languageEnglish
Title of host publicationWorking Notes Proceedings of the MediaEval 2017 Workshop, co-located with the Conference and Labs of the Evaluation Forum (CLEF 2017)
EditorsGuillaume Gravier, Benjamin Bischke, Claire-Hélène Demarty, Maia Zaharieva, Michael Riegler, Emmanuel Dellandrea, Dmitry Bogdanov, Richard Sutcliffe, Gareth J. F. Jones, Martha Larson
PublisherCEUR-WS
Number of pages3
Publication statusPublished - Sept 2017
Event2017 Multimedia Benchmark Workshop, MediaEval 2017 - Dublin, Ireland
Duration: 13 Sept 201715 Sept 2017
https://ceur-ws.org/Vol-1984/

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume1984
ISSN (Print)1613-0073

Conference

Conference2017 Multimedia Benchmark Workshop, MediaEval 2017
Country/TerritoryIreland
CityDublin
Period13/09/1715/09/17
Internet address

Scopus Subject Areas

  • Computer Science(all)

Fingerprint

Dive into the research topics of 'HKBU at MediaEval 2017 Emotional impact of movies task'. Together they form a unique fingerprint.

Cite this