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Cross-Domain Feature Learning in Multimedia
Yang, Xiaoshan; Zhang, Tianzhu; Xu, Changsheng; Xu CS(徐常胜)
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.
KeywordCross-domain Deep Learning Feature Learning Multi-modal
Indexed BySCI
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Cited Times:48[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorXu CS(徐常胜)
Recommended Citation
GB/T 7714
Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Cross-Domain Feature Learning in Multimedia[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(1):64-78.
APA Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,&徐常胜.(2015).Cross-Domain Feature Learning in Multimedia.IEEE TRANSACTIONS ON MULTIMEDIA,17(1),64-78.
MLA Yang, Xiaoshan,et al."Cross-Domain Feature Learning in Multimedia".IEEE TRANSACTIONS ON MULTIMEDIA 17.1(2015):64-78.
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