On clustering and retrieval of video shots
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Web video topic discovery and tracking via bipartite graph reinforcement model
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Event driven summarization for web videos
WSM '09 Proceedings of the first SIGMM workshop on Social media
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
Enriching and localizing semantic tags in internet videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A unified framework for web video topic discovery and visualization
Pattern Recognition Letters
Discovering hot topics from geo-tagged video
Neurocomputing
Social event detection with robust high-order co-clustering
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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As the explosive growth of web videos on video-shared sites like YouTube, the discovery of video topics has become a hot research area. In order to utilize all kinds of characteristics in web video such as visual features (SIFT, shape or color) and contextual cues (such as title or tags) effectively, this paper proposes an approach to represent the explicit and implicit correlations hidden in web videos by a star-structured K-partite graph model, and then a co-clustering process is conducted to discover video topics. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.