@inbook{dg20110601120000,
	att_abstract={Over the years the fidelity and quantity of TV content has steadily increased, but consumers
are still experiencing considerable difficulties in finding the content matching their personal
interests. New mobile and IP consumption environments have emerged with the promise
of ubiquitous delivery of desired content, but in many cases, available content descriptions
in the form of electronic program guides lack sufficient detail and cumbersome human interfaces yield a less than positive user experience. Creating metadata through a detailed
manual annotation of TV content is costly and, in many cases, this metadata may be lost
in the content life-cycle as assets are repurposed for multiple distribution channels. Content
organization can be daunting when considering domains from breaking news contributions,
local or government channels, live sports, music videos, documentaries up through dramatic
series and feature films. As the line between TV content and Internet content continues
to blur, more and more long tail content will appear on TV and the ability to be able to
automatically generate metadata for it becomes paramount. Research results from several
disciplines must be brought together to address the complex challenge of cost effectively augmenting existing content descriptions to facilitate content personalization and adaptation for
users given todays range of content consumption contexts.This chapter presents systems architectures for processing large volumes of video efficiently, practical, state of the art solutions for TV content analysis and metadata generation,
and potential applications that utilize this metadata in effective and enabling ways.
 },
	att_authors={dg1597, ab0762, zl3194, br2187, bs1261, ez2685},
	att_categories={C_CCF.10},
	att_copyright={CRC Press, Taylor Francis LLC},
	att_copyright_notice={The definitive version was published in proceedings of TV Content Analysis (CRC Press/Taylor & Francis). {{, 2012-02-22}}{{, http://mklab.iti.gr/tvca/}} },
	att_donotupload={false},
	att_private={false},
	att_projects={ATTCAE},
	att_tags={IPTV, Mobile Video, Metadata, Content based retrieval, Television},
	att_techdoc={true},
	att_techdoc_key={TD:100331},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100331_DS1_2012-02-29T15:13:12.587Z.pdf},
	author={David Gibbon and Andrea Basso and Lee Begeja and Zhu Liu and Bernard Renger and Behzad Shahraray and Eric Zavesky},
	booktitle={TV Content Analysis},
	institution={{TV Content Analysis}},
	month={June},
	publisher={CRC Press},
	title={Large-Scale Analysis for Interactive
Media Consumption},
	year=2012,
}