Exploring Large-Scale Video News via Interactive Visualization

In this paper, we have developed a novel visualization framework to enable more effective visual analysis of large-scale news videos, where keyframes and keywords are automatically extracted from news video clips and visually represented according to their interestingness measurement to help audiences rind news stories of interest at first glance. A computational approach is also developed to quantify the interestingness measurement of video clips. Our experimental results have shown that our techniques for intelligent news video analysis have the capacity to enable more effective visualization of large-scale news videos. Our news video visualization system is very useful for security applications and for general audiences to quickly find news topics of interest from among many channels

[1]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[2]  Helmut Schmidt,et al.  Probabilistic part-of-speech tagging using decision trees , 1994 .

[3]  Mark E. J. Newman,et al.  Maps and Cartograms of the 2004 US Presidential Election Results , 2005, Adv. Complex Syst..

[4]  Alan Hanjalic,et al.  Automated high-level movie segmentation for advanced video-retrieval systems , 1999, IEEE Trans. Circuits Syst. Video Technol..

[5]  Milind R. Naphade,et al.  A probabilistic framework for semantic video indexing, filtering, and retrieval , 2001, IEEE Trans. Multim..

[6]  Shin Satoh.  News video analysis based on identical shot detection , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[7]  Anil K. Jain,et al.  Automatic text location in images and video frames , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[8]  Svetha Venkatesh,et al.  Towards automatic extraction of expressive elements from motion pictures: tempo , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[9]  John R. Smith,et al.  Semantic Indexing of Multimedia Content Using Visual, Audio, and Text Cues , 2003, EURASIP J. Adv. Signal Process..

[10]  Shin'ichi Satoh,et al.  An efficient implementation and evaluation of robust face sequence matching , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[11]  Jianping Fan,et al.  Learning the semantics of images by using unlabeled samples , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Yihong Gong,et al.  Automatic parsing and indexing of news video , 1995, Multimedia Systems.

[13]  Ben Shneiderman,et al.  Tree-maps: a space-filling approach to the visualization of hierarchical information structures , 1991, Proceeding Visualization '91.

[14]  Svetha Venkatesh,et al.  Toward automatic extraction of expressive elements from motion pictures: tempo , 2002, IEEE Trans. Multim..

[15]  John R. Smith,et al.  Image Classification and Querying Using Composite Region Templates , 1999, Comput. Vis. Image Underst..

[16]  Shih-Fu Chang,et al.  Determining computable scenes in films and their structures using audio-visual memory models , 2000, ACM Multimedia.

[17]  Howard D. Wactlar,et al.  Constant density displays using diversity sampling , 2003, IEEE Symposium on Information Visualization 2003 (IEEE Cat. No.03TH8714).

[18]  Shih-Fu Chang,et al.  Semantic visual templates: linking visual features to semantics , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[19]  Paul Whitney,et al.  Multi-faceted insight through interoperable visual information analysis paradigms , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).