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003			ES-MaUEC
005			20230130200407.0
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007			cr nn 008mamaa
008			220601s2015 sz | s |||| 0|eng d
020			_a9783031022531
024	7		_a10.1007/978-3-031-02253-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1637.5 _b2015 EB
100	1		_aZhang, Qiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686358 _c(Computer scientist)
245	1	0	_aDictionary Learning in Visual Computing _cby Qiang Zhang, Baoxin Li
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XVII, 133 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Image Video and Multimedia Processing _x1559-8144
505	0		_aAcknowledgments -- Figure Credits -- Introduction -- Fundamental Computing Tasks in Sparse Representation -- Dictionary Learning Algorithms -- Applications of Dictionary Learning in Visual Computing -- An Instructive Case Study with Face Recognition -- Bibliography -- Authors' Biographies.
520			_aThe last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9667934 _aProceso de imágenes _xModelos matemáticos
650		7	_2embne _9159793 _aVisión por ordenador
700	1		_aLi, Baoxin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686359
776	0	8	_iPrinted edition: _z9783031011252
776	0	8	_iPrinted edition: _z9783031033810
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02253-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _esc _zSI