Large-Scale Geosocial Multimedia [Guest editorial]

IEEE Multim. Pub Date : 2014-07-01 DOI:10.1109/MMUL.2014.43
R. Ji, Yi Yang, N. Sebe, K. Aizawa, Liangliang Cao
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引用次数: 0

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.
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大型地理社交多媒体[客座评论]
随着Web 2.0时代的到来,在Flickr、YouTube、Facebook、Zooomr等社交网站上共享的地理多媒体数据呈爆炸式增长。无处不在的社交媒体分析中的位置感知媒体描述、建模、学习和推荐已成为计算机视觉、多媒体和信号处理协会最近研究的重点。新一代的多媒体应用程序集成了图像/视频注释、视觉搜索、内容挖掘和推荐等功能,可能会给这个领域带来革命性的变化。结合位置感知的社交多媒体的流行,位置上下文数据使传统上具有挑战性的问题变得更容易处理。本期特刊汇集了活跃的研究人员,分享这一令人兴奋的领域的最新进展。本期重点介绍了面向地理社会多媒体计算的大规模多证据学习的最新进展,并指出了几个关键挑战和潜在创新。
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