Large-scale geosocial multimedia

Ji, R; Yang, Y; Sebe, N; Aizawa, K; Cao, L

Large-scale geosocial multimedia

Ji, R Yang, Y

Sebe, N Aizawa, K Cao, L

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Publication Type:: Journal Article
Citation:: IEEE Multimedia, 2014, 21 (3), pp. 7 - 9
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Ji, R	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Sebe, N	en_US
dc.contributor.author	Aizawa, K	en_US
dc.contributor.author	Cao, L	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Multimedia, 2014, 21 (3), pp. 7 - 9	en_US
dc.identifier.issn	1070-986X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115993
dc.description.abstract	With the advance of the Web 2.0 era came an explosive growth of geographical multimedia data shared on social network websites such as Flickr, YouTube, Facebook, and Zooomr. Location-aware media description, modeling, learning, and recommendation in pervasive social media analytics have become a key focus of the recent research in computer vision, multimedia, and signal processing societies. A new breed of multimedia applications that incorporates image/video annotation, visual search, content mining and recommendation, and so on may revolutionize the field. Combined with the popularity of location-aware social multimedia, location context data makes traditionally challenging problems more tractable. This special issue brings together active researchers to share recent progress in this exciting area. This issue highlights the latest developments in large-scale multiple evidence-based learning for geosocial multimedia computing and identifies several key challenges and potential innovations. © 2014 IEEE.	en_US
dc.relation.ispartof	IEEE Multimedia	en_US
dc.relation.isbasedon	10.1109/MMUL.2014.43	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Large-scale geosocial multimedia	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	21	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

With the advance of the Web 2.0 era came an explosive growth of geographical multimedia data shared on social network websites such as Flickr, YouTube, Facebook, and Zooomr. Location-aware media description, modeling, learning, and recommendation in pervasive social media analytics have become a key focus of the recent research in computer vision, multimedia, and signal processing societies. A new breed of multimedia applications that incorporates image/video annotation, visual search, content mining and recommendation, and so on may revolutionize the field. Combined with the popularity of location-aware social multimedia, location context data makes traditionally challenging problems more tractable. This special issue brings together active researchers to share recent progress in this exciting area. This issue highlights the latest developments in large-scale multiple evidence-based learning for geosocial multimedia computing and identifies several key challenges and potential innovations. © 2014 IEEE.

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http://hdl.handle.net/10453/115993