WordNet: a lexical database for English
Communications of the ACM
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Collages as dynamic summaries for news video
Proceedings of the tenth ACM international conference on Multimedia
ThemeRiver: Visualizing Theme Changes over Time
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Language-specific models in multilingual topic tracking
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
K-means clustering via principal component analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Tracking news stories across different sources
Proceedings of the 13th annual ACM international conference on Multimedia
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
A Novel Document Clustering Model Based on Latent Semantic Analysis
SKG '07 Proceedings of the Third International Conference on Semantics, Knowledge and Grid
Web video topic discovery and tracking via bipartite graph reinforcement model
Proceedings of the 17th international conference on World Wide Web
Personalized news video recommendation
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Less talk, more rock: automated organization of community-contributed collections of concert videos
Proceedings of the 18th international conference on World wide web
Large-scale news topic tracking and key-scene ranking with video near-duplicate constraints
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
The 1st workshop on large-scale multimedia retrieval and mining (LS-MMRM'09)
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Knowledge discovery over community-sharing media: from signal to intelligence
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
A document clustering algorithm for discovering and describing topics
Pattern Recognition Letters
Topic discovery of web video using star-structured K-partite graph
Proceedings of the international conference on Multimedia
Trajectory-based visualization of web video topics
Proceedings of the international conference on Multimedia
An effective multi-clue fusion approach for web video topic detection
Proceedings of the 20th ACM international conference on Multimedia
Cross-media topic detection associated with hot search queries
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Combining supervised and unsupervised models via unconstrained probabilistic embedding
Information Sciences: an International Journal
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Together with the explosive growth of web video in sharing sites like YouTube, automatic topic discovery and visualization have become increasingly important in helping to organize and navigate such large-scale videos. Previous work dealt with the topic discovery and visualization problem separately, and did not take fully into account of the distinctive characteristics of multi-modality and sparsity in web video features. This paper tries to solve web video topic discovery problem with visualization under a single framework, and proposes a Star-structured K-partite Graph based co-clustering and ranking framework, which consists of three stages: (1) firstly, represent the web videos and their multi-model features (e.g., keyword, near-duplicate keyframe, near-duplicate aural frame, etc.) as a Star-structured K-partite Graph; (2) secondly, group videos and their features simultaneously into clusters (topics) and organize the generated clusters as a linked cluster network; (3) finally, rank each type of nodes in the linked cluster network by ''popularity'' and visualize them as a novel interface to let user interactively browse topics in multi-level scales. Experiments on a YouTube benchmark dataset demonstrate the flexibility and effectiveness of our proposed framework.