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DC Field | Value | Language |
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dc.contributor.author | Xu, L-Q | - |
dc.contributor.author | Li, Y | - |
dc.date.accessioned | 2017-02-24T11:02:47Z | - |
dc.date.available | 2003 | - |
dc.date.available | 2017-02-24T11:02:47Z | - |
dc.date.issued | 2003 | - |
dc.identifier.citation | Proceedings of IEEE International Conference on Multimedia and Expo, (ICME '03), 6-9 July, 2003, 3: pp. 485 - 488, (2003) | en_US |
dc.identifier.isbn | 0-7803-7965-9 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/14121 | - |
dc.description.abstract | We 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.extent | 485 - 488 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.source | IEEE International Conference on Multimedia and Expo | - |
dc.source | IEEE International Conference on Multimedia and Expo | - |
dc.subject | Principal component analysis | en_US |
dc.subject | Audio-visual systems | en_US |
dc.subject | Streaming media | en_US |
dc.subject | Image classification | en_US |
dc.title | Video classification using spatial-temporal features and PCA | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/ICME.2003.1221354 | - |
pubs.volume | 3 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | 314.26 kB | Adobe PDF | View/Open |
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