Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization

Cross-media retrieval has become a key problem in both research and application, in which users can search results across all of the media types (text, image, audio, video, and 3-D) by submitting a query of any media type. How to measure the content similarity among different media is the key challe...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology 2014-06, Vol.24 (6), p.965-978
Hauptverfasser:	Zhai, Xiaohua, Peng, Yuxin, Xiao, Jianguo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applied sciences Artificial intelligence Computer science control theory systems Correlation Cross-media retrieval Data processing. List processing. Character string processing Exact sciences and technology joint representation learning Joints Lakes Media Memory organisation. Data processing Multimedia communication Multimedia computer applications Semantics semi-supervised regularization Software sparse regularization Visualization
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