TV Watcher: Distributed Media Analysis and Correlation

The explosion of available content in broadcast media has created a desperate need for applications and prerequisite system architectures to support automatic capture, filtration, categorization, correlation, and higher level inferencing of streaming data from distributed sources. We present TV Watcher, an archetypical example of such an application. TV Watcher performs usercontrolled correlation of live television feeds and allows the user to automatically navigate through the available channels based on content of interest. We introduce the Symphony architecture for distributed real-time media analysis and delivery to meet the system requirements for applications with such needs. TV Watcher is built on top of the Symphony architecture, and currently uses closed-captioning information to correlate television programming. We present the results of a user study that shows the correlation engine is consistently able to pick significantly useful and relevant content.

[1]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[2]  Umakishore Ramachandran,et al.  D-Stampede: distributed programming system for ubiquitous computing , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[3]  David R. Bacher.  Content-based Indexing of Captioned Video , 1994 .

[4]  Roger B. Dannenberg.  Aura as a Platform for Distributed Sensing and Control , 2002 .

[5]  Alexander G. Hauptmann,et al.  Informedia: news-on-demand multimedia information acquisition and retrieval , 1997 .

[6]  John P. Oakley,et al.  Storage and Retrieval for Image and Video Databases , 1993 .

[7]  Pattie Maes,et al.  Just-in-time information retrieval , 2000 .

[8]  Marcel Worring,et al.  Multimodal Video Indexing : A Review of the State-ofthe-art , 2001 .

[9]  Ramesh C. Jain.  Experiential computing , 2003, CACM.

[10]  Takeo Kanade,et al.  Intelligent Access to Digital Video: Informedia Project , 1996, Computer.

[11]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[12]  Umakishore Ramachandran,et al.  An Architecture for Pervasive Computing , 2004 .

[13]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[14]  Roger B. Dannenberg,et al.  A System Supporting Flexible Distributed Real-Time Music Processing , 2001, ICMC.

[15]  Alex Pentland,et al.  Photobook: tools for content-based manipulation of image databases , 1994, Other Conferences.

[16]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[17]  Mubarak Shah,et al.  Scene detection in Hollywood movies and TV shows , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[18]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[19]  Michael J. Swain,et al.  AltaVista Media Search: Indexing Multimedia for the Internet , 1999 .

[20]  Stephen Wolfram,et al.  In Partial Fulfillment of the Requirements for the degree of , 1980 .

[21]  Michael J. Witbrock,et al.  Story segmentation and detection of commercials in broadcast news video , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.

[22]  James Allan,et al.  Automatic structuring and retrieval of large text files , 1994, CACM.

[23]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.