Mining Large-Scale News Video Database Via Knowledge Visualization

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.

[1]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[2]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[3]  Jianping Fan,et al.  Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing , 2004, IEEE Transactions on Image Processing.

[4]  Wei-Ying Ma,et al.  Image and Video Retrieval , 2003, Lecture Notes in Computer Science.

[5]  Lucy T. Nowell,et al.  ThemeRiver: visualizing theme changes over time , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[6]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[7]  Jianping Fan,et al.  Exploring Large-Scale Video News via Interactive Visualization , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[8]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Jarke J. van Wijk,et al.  Bridging the Gaps , 2006, IEEE Computer Graphics and Applications.

[10]  Avideh Zakhor,et al.  Applications of Video-Content Analysis and Retrieval , 2002, IEEE Multim..

[11]  Alexander G. Hauptmann.  Lessons for the Future from a Decade of Informedia Video Analysis Research , 2005, CIVR.

[12]  Jianping Fan,et al.  Multi-level annotation of natural scenes using dominant image components and semantic concepts , 2004, MULTIMEDIA '04.