000			03727nam a22005415i 4500
001			978-0-387-69942-4
003			DE-He213
005			20161121230610.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387699424 _9978-0-387-69942-4
024	7		_a10.1007/978-0-387-69942-4 _2doi
050		4	_aQA76.575
072		7	_aUG _2bicssc
072		7	_aCOM034000 _2bisacsh
082	0	4	_a006.7 _223
100	1		_aGong, Yihong. _eauthor.
245	1	0	_aMachine Learning for Multimedia Content Analysis _h[electronic resource] / _cby Yihong Gong, Wei Xu.
264		1	_aBoston, MA : _bSpringer US, _c2007.
300			_aXVI, 277 p. 20 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aUnsupervised Learning -- Dimension Reduction -- Data Clustering Techniques -- Generative Graphical Models -- of Graphical Models -- Markov Chains and Monte Carlo Simulation -- Markov Random Fields and Gibbs Sampling -- Hidden Markov Models -- Inference and Learning for General Graphical Models -- Discriminative Graphical Models -- Maximum Entropy Model and Conditional Random Field -- Max-Margin Classifications.
520			_aChallenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly. Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons. Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry. .
650		0	_aComputer science.
650		0	_aDatabase management.
650		0	_aInformation storage and retrieval.
650		0	_aMultimedia information systems.
650		0	_aArtificial intelligence.
650		0	_aComputer graphics.
650		0	_aApplication software.
650	1	4	_aComputer Science.
650	2	4	_aMultimedia Information Systems.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aComputer Applications.
650	2	4	_aInformation Storage and Retrieval.
650	2	4	_aDatabase Management.
650	2	4	_aComputer Graphics.
700	1		_aXu, Wei. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387699387
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-69942-4
912			_aZDB-2-SCS
950			_aComputer Science (Springer-11645)
999			_c501145 _d501145