Issue No. 03 - July-Sept. (2014 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MMUL.2014.43
Rongrong Ji , Xiamen University
Yi Yang , University of Queensland
Nicu Sebe , University of Trento
Kiyoharu Aizawa , University of Tokyo
Liangliang Cao , IBM Watson Research Center
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.
Special issues and sections, Social network services, Geography, Information services, Multimedia communication, Streaming media, Media, Videos, Blogs, Geographic information systems, Geospatial analysis
R. Ji, Y. Yang, N. Sebe, K. Aizawa and L. Cao, "Large-Scale Geosocial Multimedia [Guest editorial]," in IEEE MultiMedia, vol. 21, no. 3, pp. 7-9, 2014.