| 000 | 04001nam a22005175i 4500 | ||
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| 001 | 978-3-031-02253-1 | ||
| 003 | DE-He213 | ||
| 005 | 20240730164655.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2015 sz | s |||| 0|eng d | ||
| 020 |
_a9783031022531 _9978-3-031-02253-1 |
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| 024 | 7 |
_a10.1007/978-3-031-02253-1 _2doi |
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| 050 | 4 | _aT1-995 | |
| 072 | 7 |
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_a620 _223 |
| 100 | 1 |
_aZhang, Qiang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _985606 |
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| 245 | 1 | 0 |
_aDictionary Learning in Visual Computing _h[electronic resource] / _cby Qiang Zhang, Baoxin Li. |
| 250 | _a1st ed. 2015. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
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| 300 |
_aXVII, 133 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSynthesis Lectures on Image, Video, and Multimedia Processing, _x1559-8144 |
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| 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/extensionsof 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. | ||
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_aEngineering. _99405 |
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| 650 | 0 |
_aElectrical engineering. _985608 |
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| 650 | 0 |
_aSignal processing. _94052 |
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| 650 | 1 | 4 |
_aTechnology and Engineering. _985609 |
| 650 | 2 | 4 |
_aElectrical and Electronic Engineering. _985610 |
| 650 | 2 | 4 |
_aSignal, Speech and Image Processing. _931566 |
| 700 | 1 |
_aLi, Baoxin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _985613 |
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| 710 | 2 |
_aSpringerLink (Online service) _985615 |
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| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9783031011252 |
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_iPrinted edition: _z9783031033810 |
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_aSynthesis Lectures on Image, Video, and Multimedia Processing, _x1559-8144 _985616 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-02253-1 |
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| 942 | _cEBK | ||
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