Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Web video categorization based on Wikipedia categories and content-duplicated open resources
Proceedings of the international conference on Multimedia
Semantic grounding of hybridization for tag recommendation
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Knowing funny: genre perception and categorization in social video sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multimedia Tools and Applications
Tag suggestion and localization for web videos by bipartite graph matching
WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
SocialTransfer: cross-domain transfer learning from social streams for media applications
Proceedings of the 20th ACM international conference on Multimedia
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Tag recommendation is a common way to enrich the textual annotation of multimedia contents. However, state-of-the-art recommendation methods are built upon the pair-wised tag relevance, which hardly capture the context of the web video, i.e., when who are doing what at where. In this paper we propose the context-oriented tag recommendation (CtextR) approach, which expands tags for web videos under the context-consistent constraint. Given a web video, CtextR first collects the multi-form WWW resources describing the same event with the video, which produce an informative and consistent context; and then, the tag recommendation is conducted based on the obtained context. Experiments on an 80,031 web video collection show CtextR recommends various relevant tags to web videos. Moreover, the enriched tags improve the performance of web video categorization.