Adaptive Local Hyperplanes for MTV affective analysis

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dc.contributor.author Xu, M
dc.contributor.author Chen, L
dc.contributor.author He, S
dc.contributor.author Xu, C
dc.contributor.author Jin, J
dc.contributor.editor NA
dc.date.accessioned 2012-02-02T11:07:29Z
dc.date.issued 2010-01
dc.identifier.citation Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10, 2010, pp. 167 - 170
dc.identifier.isbn 978-1-4503-0460-3
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
dc.publisher ACM
dc.relation.isbasedon 10.1145/1937728.1937768
dc.title Adaptive Local Hyperplanes for MTV affective analysis
dc.type Conference Proceeding
dc.parent Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10
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.conference International Conference on Internet Multimedia Computing and Service
dc.for 080305 Multimedia Programming
dc.personcode 990421
dc.personcode 108889
dc.personcode 109684
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.date.activity 2010-12-30
dc.location.activity Harbin, China en_US
dc.location.activity ISI:000166373600002
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
dc.description.keywords Reliability Evaluation
dc.description.keywords Probability
dc.description.keywords Complexity
dc.description.keywords Systems
dc.description.keywords Terms
dc.description.keywords Affective factors
dc.description.keywords Classification approach
dc.description.keywords Discriminative ability
dc.description.keywords Experimentation
dc.description.keywords Feature weight
dc.description.keywords Machine learning algorithms
dc.description.keywords Machine learning methods
dc.description.keywords Multi-class classification
dc.description.keywords Geometry
dc.description.keywords Internet
dc.description.keywords Learning algorithms
dc.description.keywords Learning systems
dc.description.keywords Experiments
pubs.embargo.period Not known
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 Computing and Communications
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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