Discriminative Tag Learning on YouTube Videos with Latent Sub-tags
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.
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.
Research Areas
