Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Computer
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the ACM International Conference on Image and Video Retrieval
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Semantic event detection in structured video using hybrid HMM/SVM
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Semantic Home Photo Categorization
IEEE Transactions on Circuits and Systems for Video Technology
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The automatic detection of semantic concepts is a key technology for enabling efficient and effective video content management. Conventional techniques for semantic concept detection in video content still suffer from several interrelated issues: the semantic gap, the imbalanced data set problem, and a limited concept vocabulary size. In this paper, we propose to perform semantic concept detection for user-created video content using an image folksonomy in order to overcome the aforementioned problems. First, an image folksonomy contains a vast amount of user-contributed images. Second, a significant portion of these images has been manually annotated by users using a wide variety of tags. However, user-supplied annotations in an image folksonomy are often characterized by a high level of noise. Therefore, we also discuss a method that allows reducing the number of noisy tags in an image folksonomy. This tag refinement method makes use of tag co-occurrence statistics. To verify the effectiveness of the proposed video content annotation system, experiments were performed with user-created image and video content available on a number of social media applications. For the datasets used, video annotation with tag refinement has an average recall rate of 84% and an average precision of 75%, while video annotation without tag refinement shows an average recall rate of 78% and an average precision of 62%.