Adaptive Local Hyperplanes for MTV affective analysis

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dc.contributor.author Xu, Min en_US
dc.contributor.author Chen, Ling en_US
dc.contributor.author Xu, Chengchao en_US
dc.contributor.author Jin, Jesse Sheng en_US
dc.contributor.author He, Sean en_US
dc.contributor.editor NA en_US
dc.date.accessioned 2012-02-02T11:07:29Z
dc.date.available 2012-02-02T11:07:29Z
dc.date.issued 2010 en_US
dc.identifier 2010003112 en_US
dc.identifier.citation Xu Min et al. 2010, 'Adaptive Local Hyperplanes for MTV affective analysis', , ACM, United States, , pp. 167-170. en_US
dc.identifier.issn 978-1-4503-0460-3 en_US
dc.identifier.other E1 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 en_US
dc.language en_US
dc.publisher ACM en_US
dc.relation.isbasedon http://dx.doi.org/10.1145/1937728.1937768 en_US
dc.title Adaptive Local Hyperplanes for MTV affective analysis en_US
dc.parent Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10 en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation United States en_US
dc.identifier.startpage 167 en_US
dc.identifier.endpage 170 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080305 en_US
dc.personcode 109684 en_US
dc.personcode 108889 en_US
dc.personcode 990421 en_US
dc.personcode 00027228 en_US
dc.personcode 0000022523 en_US
dc.percentage 100 en_US
dc.classification.name Multimedia Programming en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Internet Multimedia Computing and Service en_US
dc.date.activity 20101230 en_US
dc.location.activity Harbin, China en_US
dc.description.keywords Affective factors; Classification approach; Discriminative ability; Experimentation; Feature weight; Machine learning algorithms; Machine learning methods; Multi-class classification; Geometry; Internet; Learning algorithms; Learning systems; Experiments en_US


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