Semi-supervised cross feature learning for semantic concept detection in videos

  title={Semi-supervised cross feature learning for semantic concept detection in videos},
  author={Rong Yan and Milind R. Naphade},
  journal={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  pages={657-663 vol. 1}
For large scale automatic semantic video characterization, it is necessary to learn and model a large number of semantic concepts. But a major obstacle to this is the insufficiency of labeled training samples. Multi-view semi-supervised learning algorithms such as co-training may help by incorporating a large amount of unlabeled data. However, one of their assumptions requiring that each view be sufficient for learning is usually violated in semantic concept detection. In this paper, we propose… CONTINUE READING
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