Towards learning emotional subspace

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

Towards learning emotional subspace

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

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Working Notes Proceedings of the MediaEval 2018 Workshop
Editors	Martha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones
Publisher	CEUR-WS
Number of pages	3
Publication status	Published - Oct 2018
Event	MediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France Duration: 29 Oct 2018 → 31 Oct 2018 https://ceur-ws.org/Vol-2283/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
Volume	2283
ISSN (Print)	1613-0073

Conference

Conference	MediaEval 2018: Multimedia Benchmark Workshop
Country/Territory	France
City	Sophia Antipolis
Period	29/10/18 → 31/10/18
Internet address	https://ceur-ws.org/Vol-2283/

Scopus Subject Areas

Computer Science(all)

Access to Document

http://ceur-ws.org/Vol-2283/MediaEval_18_paper_45.pdf

Cite this

@inproceedings{308bf45573d14cc8adc50de39d362809,

title = "Towards learning emotional subspace",

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).",

author = "Ko, {Tobey H.} and Zhonglei Gu and Tiantian He and Yang Liu",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.; MediaEval 2018: Multimedia Benchmark Workshop ; Conference date: 29-10-2018 Through 31-10-2018",

year = "2018",

month = oct,

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS",

editor = "Martha Larson and Piyush Arora and Claire-H{\'e}l{\`e}ne Demarty and Michael Riegler and Benjamin Bischke and Emmanuel Dellandrea and Mathias Lux and Alastair Porter and Jones, {Gareth J. F.}",

booktitle = "Working Notes Proceedings of the MediaEval 2018 Workshop",

url = "https://ceur-ws.org/Vol-2283/",

}

Ko, TH, Gu, Z, He, T & Liu, Y 2018, Towards learning emotional subspace. in M Larson, P Arora, C-H Demarty, M Riegler, B Bischke, E Dellandrea, M Lux, A Porter & GJF Jones (eds), Working Notes Proceedings of the MediaEval 2018 Workshop. CEUR Workshop Proceedings, vol. 2283, CEUR-WS, MediaEval 2018: Multimedia Benchmark Workshop, Sophia Antipolis, France, 29/10/18. <http://ceur-ws.org/Vol-2283/MediaEval_18_paper_45.pdf>

Towards learning emotional subspace. / Ko, Tobey H.; Gu, Zhonglei; He, Tiantian et al.

Working Notes Proceedings of the MediaEval 2018 Workshop. ed. / Martha Larson; Piyush Arora; Claire-Hélène Demarty; Michael Riegler; Benjamin Bischke; Emmanuel Dellandrea; Mathias Lux; Alastair Porter; Gareth J. F. Jones. CEUR-WS, 2018. (CEUR Workshop Proceedings; Vol. 2283).

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Towards learning emotional subspace

AU - Ko, Tobey H.

AU - Gu, Zhonglei

AU - He, Tiantian

AU - Liu, Yang

N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61503317, in part by the General Research Fund (GRF) from the Research Grant Council (RGC) of Hong Kong SAR under Project HKBU12202417, and in part by the SZSTI Grant with the Projct Code JCYJ20170307161544087.

PY - 2018/10

Y1 - 2018/10

N2 - 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).

AB - 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).

UR - http://ceur-ws.org/Vol-2283/

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M3 - Conference contribution

AN - SCOPUS:85059864235

T3 - CEUR Workshop Proceedings

BT - Working Notes Proceedings of the MediaEval 2018 Workshop

A2 - Larson, Martha

A2 - Arora, Piyush

A2 - Demarty, Claire-Hélène

A2 - Riegler, Michael

A2 - Bischke, Benjamin

A2 - Dellandrea, Emmanuel

A2 - Lux, Mathias

A2 - Porter, Alastair

A2 - Jones, Gareth J. F.

PB - CEUR-WS

T2 - MediaEval 2018: Multimedia Benchmark Workshop

Y2 - 29 October 2018 through 31 October 2018

ER -

Towards learning emotional subspace

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