Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genre-specific semantic video indexing
Proceedings of the ACM International Conference on Image and Video Retrieval
Web video categorization based on Wikipedia categories and content-duplicated open resources
Proceedings of the international conference on Multimedia
Content-based video genre classification using multiple cues
Proceedings of the 3rd international workshop on Automated information extraction in media production
Tag suggestion and localization in user-generated videos based on social knowledge
Proceedings of second ACM SIGMM workshop on Social media
ShotTagger: tag location for internet videos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Automatic concept-to-query mapping for web-based concept detector training
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Improving video classification via youtube video co-watch data
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Multimodal genre classification of TV programs and YouTube videos
Multimedia Tools and Applications
Exploiting socially-generated side information in dimensionality reduction
Proceedings of the 2nd international workshop on Socially-aware multimedia
Recognizing human-human interaction activities using visual and textual information
Pattern Recognition Letters
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Web video categorization is a fundamental task for web video search. In this paper, we explore the Google challenge from a new perspective by combing contextual and social information under the scenario of social web. The semantic meaning of text (title and tags), video relevance from related videos, and user interest induced from user videos, are integrated to robustly determine the video category. Experiments on YouTube videos demonstrate the effectiveness of the proposed solution. The performance reaches 60% improvement compared to the traditional text based classifiers.