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dc.contributor.authorXu, L-Q-
dc.contributor.authorLi, Y-
dc.identifier.citationProceedings of IEEE International Conference on Multimedia and Expo, (ICME '03), 6-9 July, 2003, 3: pp. 485 - 488, (2003)en_US
dc.description.abstractWe investigate the problem of automated video classification by analysing the low-level audio-visual signal patterns along the time course in a holistic manner. Five popular TV broadcast genre are studied including sports, cartoon, news, commercial and music. A novel statistically based approach is proposed comprising two important ingredients designed for implicit semantic content characterisation and class identities modelling. First, a spatial-temporal audio-visual "concatenated" feature vector is composed, aiming to capture crucial clip-level video structure information inherent in a video genre. Second, the feature vector is further processed using principal component analysis to reduce the spatial-temporal redundancy while exploiting the correlations between feature elements. This gives rise to a compact representation fro effective probabilistic modelling of each video genre. Extensive experiments are conducted assessing various aspects of the approach and their influence on the overall system performance.en_US
dc.format.extent485 - 488-
dc.sourceIEEE International Conference on Multimedia and Expo-
dc.sourceIEEE International Conference on Multimedia and Expo-
dc.subjectPrincipal component analysisen_US
dc.subjectAudio-visual systemsen_US
dc.subjectStreaming mediaen_US
dc.subjectImage classificationen_US
dc.titleVideo classification using spatial-temporal features and PCAen_US
dc.typeConference Paperen_US
Appears in Collections:Dept of Computer Science Research Papers

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