Multi-Modal Video Concept Extraction Using Co-Training

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Multi-Modal Video Concept Extraction Using Co-Training
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. A major obstacle to this is the insufficiency of labeled training samples. Semi-supervised learning algorithms such as cotraining may help by incorporating a large amount of unlabeled data, which allows the redundant information across views to improve the learning performance. Although co-training has been successfully applied in several domains, it has not been used to detect video concepts before. In this paper, we extend co-training to the domain of video concept detection and investigate different strategies of co-training as well as their effects to the detection accuracy. We demonstrate performance based on the guideline of the TRECVID’03 semantic concept extraction task.
Rong Yan, Milind R. Naphade
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Authors Rong Yan, Milind R. Naphade
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