A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Semantic context transfer across heterogeneous sources for domain adaptive video search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Ranking with local regression and global alignment for cross media retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Video2Text: Learning to Annotate Video Content
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
IEEE Transactions on Knowledge and Data Engineering
Probabilistic visual concept trees
Proceedings of the international conference on Multimedia
Active learning in multimedia annotation and retrieval: A survey
ACM Transactions on Intelligent Systems and Technology (TIST)
Towards cross-category knowledge propagation for learning visual concepts
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Effective transfer tagging from image to video
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Web videos such as YouTube are hard to obtain sufficient precisely labeled training data and analyze due to the complex ontology. To deal with these problems, we present a hierarchical web video classification framework by learning heterogeneous web data, and construct a bottom-up semantic forest of video concepts by learning from meta-data. The main contributions are two-folds: firstly, analysis about middle-level concepts' distribution is taken based on data collected from web communities, and a concepts redistribution assumption is made to build effective transfer learning algorithm. Furthermore, an AdaBoost-Like transfer learning algorithm is proposed to transfer the knowledge learned from Flickr images to YouTube video domain and thus it facilitates video classification. Secondly, a group of hierarchical taxonomies named Semantic Forest are mined from YouTube and Flickr tags which reflect better user intention on the semantic level. A bottom-up semantic integration is also constructed with the help of semantic forest, in order to analyze video content hierarchically in a novel perspective. A group of experiments are performed on the dataset collected from Flickr and YouTube. Compared with state-of-the-arts, the proposed framework is more robust and tolerant to web noise.