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TV news story segmentation, personalisation and recommendation

Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389, Lee, Hyowon orcid logoORCID: 0000-0003-4395-7702, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135, Marlow, Seán and Murphy, Noel (2003) TV news story segmentation, personalisation and recommendation. In: AAAI 2003 Spring Symposium on Intelligent Multimedia Knowledge Management, 24-26 March 2003, Stanford University, Palo Alto, CA.

Abstract
Large volumes of information in video format are being created and made available from a number of application areas, including movies, broadcast TV, CCTV, education video materials, and so on. As this information is increasingly in digital format, this creates the opportunity and then the demand for content-based access to such material. One particular kind of video information that we are interested in is broadcast TV news and in this paper we report on our work on developing content-based access to broadcast TV news. Our work is carried out within the context of the Físchlár system, developed to allow content access to large volumes of digital video information. We report our work on Físchlár-News which provides text search based on closed caption information as well as our on-going work on segmenting TV News programmes and providing personalised intelligent access to TV news stories, on fixed as well as mobile platforms.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Digital video
Computer Science > Information retrieval
DCU Faculties and Centres:Research Institutes and Centres > Centre for Digital Video Processing (CDVP)
Publisher:Association for the Advancement of Artificial Intelligence
Official URL:http://www.aaai.org/Symposia/Spring/sss03.php
Copyright Information:©AAAI
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Enterprise Ireland
ID Code:348
Deposited On:14 Mar 2008 by DORAS Administrator . Last Modified 08 Nov 2018 11:20
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