Discriminative Tag Learning on YouTube Videos with Latent Sub-tags

Computer Vision and Pattern Recognition, IEEE(2011)


We consider the problem of content-based automated tag learning. In particular, we address semantic varia- tions (sub-tags) of the tag. Each video in the training set is assumed to be associated with a sub-tag label, and we treat this sub-tag label as latent information. A latent learning framework based on LogitBoost is proposed which jointly considers both tag label and the latent sub-tag label. The latent sub-tag information is exploited in our frame- work to assist the learning of our end goal, i.e., tag predic- tion. We use the cowatch information to initialize the learn- ing process. In experiments, we show that the proposed method achieves significantly better results over baselines on a large-scale testing video set which contains about 50 million YouTube videos.

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