Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Discriminative Training for Object Recognition Using Image Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
A system that learns to tag videos by watching youtube
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Solving the label resolution problem in supervised video content classification
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Knowledge discovery over community-sharing media: from signal to intelligence
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Can social tagged images aid concept-based video search?
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Relevance filtering meets active learning: improving web-based concept detectors
Proceedings of the international conference on Multimedia information retrieval
New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
Learning automatic concept detectors from online video
Computer Vision and Image Understanding
ShotTagger: tag location for internet videos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Reliability and effectiveness of clickthrough data for automatic image annotation
Multimedia Tools and Applications
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
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A key problem with the automatic detection of semantic concepts (like 'interview' or 'soccer') in video streams is the manual acquisition of adequate training sets. Recently, we have proposed to use online videos downloaded from portals like youtube.com for this purpose, whereas tags provided by users during video upload serve as ground truth annotations. The problem with such training data is that it is weakly labeled: Annotations are only provided on video level, and many shots of a video may be "non-relevant", i.e. not visually related to a tag. In this paper, we present a probabilistic framework for learning from such weakly annotated training videos in the presence of irrelevant content. Thereby, the relevance of keyframes is modeled as a latent random variable that is estimated during training. In quantitative experiments on real-world online videos and TV news data, we demonstrate that the proposed model leads to a significantly increased robustness with respect to irrelevant content, and to a better generalization of the resulting concept detectors.