Discriminative Tag Learning on YouTube Videos with Latent Sub-tags
Venue
Computer Vision and Pattern Recognition, IEEE (2011)
Publication Year
2011
Authors
Weilong Yang, George Toderici
BibTeX
Abstract
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.
