Automatic recognition of film genres
Proceedings of the third ACM international conference on Multimedia
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
News video classification using SVM-based multimodal classifiers and combination strategies
Proceedings of the tenth ACM international conference on Multimedia
The Journal of Machine Learning Research
Bridging the Gap: A Genre Analysis of Weblogs
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
A bootstrapping framework for annotating and retrieving WWW images
Proceedings of the 12th annual ACM international conference on Multimedia
Joint categorization of queries and clips for web-based video search
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Multi-modality web video categorization
Proceedings of the international workshop on Workshop on multimedia information retrieval
Predicting response to political blog posts with topic models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Video2Text: Learning to Annotate Video Content
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Text-based video content classification for online video-sharing sites
Journal of the American Society for Information Science and Technology
Content-enriched classifier for web video classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Distributed training strategies for the structured perceptron
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Detecting controversial events from twitter
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Model news relatedness through user comments
Proceedings of the 21st international conference companion on World Wide Web
Effective web video clustering using playlist information
Proceedings of the 27th Annual ACM Symposium on Applied Computing
SocialTransfer: cross-domain transfer learning from social streams for media applications
Proceedings of the 20th ACM international conference on Multimedia
On the prediction of popularity of trends and hits for user generated videos
Proceedings of the sixth ACM international conference on Web search and data mining
Exploiting user comments for audio-visual content indexing and retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Features with feelings: incorporating user preferences in video categorization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Fashion-focused creative commons social dataset
Proceedings of the 4th ACM Multimedia Systems Conference
Leveraging viewer comments for mood classification of music video clips
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Enriching media fragments with named entities for video classification
Proceedings of the 22nd international conference on World Wide Web companion
Comment-based multi-view clustering of web 2.0 items
Proceedings of the 23rd international conference on World wide web
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We consider the task of assigning categories (e.g., howto/cooking, sports/basketball, pet/dogs) to YouTube videos from video and text signals. We show that two complementary views on the data -- from the video and text perspectives -- complement each other and refine predictions. The contributions of the paper are threefold: (1) we show that a text-based classifier trained on imperfect predictions of the weakly supervised video content-based classifier is not redundant; (2) we demonstrate that a simple model which combines the predictions made by the two classifiers outperforms each of them taken independently; (3) we analyse such sources of text information as video title, description, user tags and viewers' comments and show that each of them provides valuable clues to the topic of the video.