Adaptive Local Hyperplanes for MTV affective analysis

Xu, M; Chen, L; He, X; Xu, C; Jin, JS

Adaptive Local Hyperplanes for MTV affective analysis

Xu, M

Chen, L

He, X

Xu, C Jin, JS

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10, 2010, pp. 167 - 170
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Jin, JS	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10, 2010, pp. 167 - 170	en_US
dc.identifier.isbn	9781450304603	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16189
dc.description.abstract	Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories; ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis. Copyright 2010 ACM.	en_US
dc.relation.ispartof	Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10	en_US
dc.relation.isbasedon	10.1145/1937728.1937768	en_US
dc.title	Adaptive Local Hyperplanes for MTV affective analysis	en_US
dc.type	Conference Proceeding
utslib.for	080305 Multimedia Programming	en_US
dc.location.activity	Harbin, China	en_US
dc.location.activity	ISI:000166373600002
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Affective analysis attracts increasing attention in multimedia domain since affective factors directly reflect audiences' attention, evaluation and memory. Existing study focuses on mapping low-level affective features to high-level emotions by applying machine learning methods. Therefore, choosing effective features and developing efficient machine learning algorithms become vital for affective analysis. In this paper, we investigate the effectiveness of a novel classification approach, called Adaptive Local Hyperplanes (ALH), in affective analysis. The reason ALH is appealing in affective analysis is two-fold. Firstly, affective features are not equally important for emotion categories; ALH inherently assigns feature weights based on discriminative ability of each feature. Secondly, ALH achieves competitive performance with state-of-the-art classifiers (e.g., SVM) while it is designed for multi-class classification. Consequently, it is worthwhile to explore the usage of ALH in affective analysis. MTV data are used in this study. As the first effort of applying ALH to affective analysis, the results presented in this paper provide a foundation for future research in affective analysis. Copyright 2010 ACM.

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http://hdl.handle.net/10453/16189