Cross-Domain Feature Learning in Multimedia
Abstract:
In the Web 2.0 era, a huge number of media data,
such as text, image/video, and social interaction information,
have been generated on the social media sites (e.g., Facebook,
Google, Flickr, and YouTube).
These media data can be effectively
adopted for many applications (e.g., image/video annotation,
image/video retrieval, and event classification) in multimedia.
However, it is difficult to design an effective feature representation
to describe these data because they have multi-modal property
(e.g., text, image, video, and audio) and multi-domain property
(e.g., Flickr, Google, and YouTube).
To deal with these issues, we
propose a novel cross-domain feature learning (CDFL) algorithm
based on stacked denoising auto-encoders. By introducing the
modal correlation constraint and the cross-domain constraint
in conventional auto-encoder, our CDFL can maximize the
correlations among different modalities and extract domain invariant
semantic features simultaneously.
To evaluate our CDFL
algorithm, we apply it to three important applications: sentiment
classification, spam filtering, and event classification.
Comprehensive
evaluations demonstrate the encouraging performance of the
proposed approach.
POEM
My Bonnie lies over the ocean.
My Bonnie lies over the sea.
My Bonnie lies over the ocean.
oh, bring back my Bonnie to me.
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