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100   $a20220601d2015      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aDictionary Learning in Visual Computing$b[electronic resource] /$fby Qiang Zhang, Baoxin Li
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (XVII, 133 p.) 
225 1 $aSynthesis Lectures on Image, Video, and Multimedia Processing,$x1559-8144
311   $a3-031-01125-2 
327   $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 .
330   $aThe last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.
410  0$aSynthesis Lectures on Image, Video, and Multimedia Processing,$x1559-8144
606   $aEngineering
606   $aElectrical engineering
606   $aSignal processing
606   $aTechnology and Engineering
606   $aElectrical and Electronic Engineering
606   $aSignal, Speech and Image Processing 
615  0$aEngineering.
615  0$aElectrical engineering.
615  0$aSignal processing.
615 14$aTechnology and Engineering.
615 24$aElectrical and Electronic Engineering.
615 24$aSignal, Speech and Image Processing .
676   $a620
700   $aZhang$b Qiang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0433648
702   $aLi$b Baoxin$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910822885403321
996   $aDictionary Learning in Visual Computing$94119931
997   $aUNINA