Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Tagging over time: real-world image annotation by lightweight meta-learning
Proceedings of the 15th international conference on Multimedia
Foundations and Trends in Information Retrieval
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Video2Text: Learning to Annotate Video Content
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
TubeTagger - YouTube-based Concept Detection
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Video corpus annotation using active learning
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Large-scale image classification: Fast feature extraction and SVM training
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Linking visual concept detection with viewer demographics
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Analysis and forecasting of trending topics in online media streams
Proceedings of the 21st ACM international conference on Multimedia
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We present a novel approach towards automatic vocabulary selection for video concept detection. Our key idea is to expand concept vocabularies with trending topics that we mine automatically on other media like Wikipedia or Twitter. We evaluate several strategies for extending concept detection to auto-detect these topics in new videos, either by linking them to a static concept vocabulary, by a visual learning of trends on the fly, or by an expansion of the vocabulary. Our study on 6,800 YouTube clips and the top 23 target trends (covering a timespan of 6 months) demonstrates that a direct visual classification of trends (by a "live" learning on trend videos) outperforms an inference from static vocabularies. However, further improvements can be achieved by a combination of both approaches.