Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/532
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dc.contributor.authorYazbek, Georgeen_US
dc.contributor.authorKfoury, Georgesen_US
dc.contributor.authorAlam, Gabrielen_US
dc.contributor.authorMokbel, Chaficen_US
dc.contributor.authorChollet, Gérarden_US
dc.date.accessioned2020-12-23T08:31:59Z-
dc.date.available2020-12-23T08:31:59Z-
dc.date.issued2007-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/532-
dc.description.abstractWe describe a high level feature extraction system for video. Video sequences are modeled using Gaussian Mixture Models. We have used those models in the past to segment video sequences into 2D+ time objects. The segmentation result has been used with great success in a compression scheme. In the present work, the Gaussian components of the model are considered to completely model the corresponding objects in the video. Their parameters are used as low-level features for a high-level model used for the detection of a topic or high level feature. The system is not optimized for a particular feature and is thus scalable to any number of features. A threshold is manually selected for each feature after normalization. The only difference between runs was normalization. We tested two runs: B_ENST_1 uses znorm per topic per video. B_ENST_2 use znorm per video. The second system provided better results.en_US
dc.format.extent3 p.en_US
dc.language.isoengen_US
dc.titleENST/UOB/LU@ TRECVID2007 high level feature extraction using 2-level piecewise GMMen_US
dc.typeConference Paperen_US
dc.relation.conferenceTRECVID Conference (Nov 2007 : USA)en_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.date.catalogued2019-07-04-
dc.description.statusPublisheden_US
dc.identifier.OlibID192665-
dc.identifier.openURLhttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.189.4448&rep=rep1&type=pdfen_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Electrical Engineering
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