Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
ThemeRiver: Visualizing Theme Changes over Time
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Multi-level annotation of natural scenes using dominant image components and semantic concepts
Proceedings of the 12th annual ACM international conference on Multimedia
IEEE Computer Graphics and Applications
Lessons for the future from a decade of informedia video analysis research
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing
IEEE Transactions on Image Processing
ODMCA: An adaptive data mining control algorithm in multicarrier networks
Computer Communications
| Hi-index | 0.00 |
In this paper, a novel framework is proposed to enable intuitive mining and exploration of large-scale video news databases via knowledge visualization. Our framework focuses on two difficult problems: (1) how to extract the most useful knowledge from the large amount of common, uninteresting knowledge of large-scale video news databases, and (2) how to present the knowledge to the users intuitively. To resolve the two problems, the interactive database exploration procedure is modeled at first. Then, optimal visualization scheme and knowledge extraction algorithm are derived from the model. To support the knowledge extraction and visualization, a statistical video analysis algorithm is proposed to extract the semantics from the video reports.