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|Title:||ENST/UOB/LU@ TRECVID2007 high level feature extraction using 2-level piecewise GMM||Authors:||Yazbek, George
|Affiliations:||Department of Electrical Engineering||Issue Date:||2007||Conference:||TRECVID Conference (Nov 2007 : USA)||Abstract:||
We 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.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/532||Open URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Electrical Engineering|
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