000 06339nam a2200805 i 4500
001 6828191
003 IEEE
005 20200413152914.0
006 m eo d
007 cr cn |||m|||a
008 140520s2014 caua foab 000 0 eng d
020 _a9781627053600
_qebook
020 _z9781627053594
_qpaperback
024 7 _a10.2200/S00563ED1V01Y201401IVM015
_2doi
035 _a(CaBNVSL)swl00403380
035 _a(OCoLC)880357632
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTA1637.5
_b.T455 2014
082 0 4 _a006.6
_223
090 _a
_bMoCl
_e201401IVM015
100 1 _aThiagarajan, Jayaraman Jayaraman.,
_eauthor.
245 1 0 _aImage understanding using sparse representations /
_cJayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias.
264 1 _aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
_bMorgan & Claypool,
_c2014.
300 _a1 PDF (xi, 106 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on image, video, and multimedia processing,
_x1559-8144 ;
_v# 15
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
500 _aSeries from website.
504 _aIncludes bibliographical references (pages 91-104).
505 0 _a1. Introduction -- 1.1 Modeling natural images -- 1.2 Natural image statistics -- 1.3 Sparseness in biological vision -- 1.4 The generative model for sparse coding -- 1.5 Sparse models for image reconstruction -- 1.5.1 Dictionary design -- 1.5.2 Example applications -- 1.6 Sparse models for recognition -- 1.6.1 Discriminative dictionaries -- 1.6.2 Bag of words and its generalizations -- 1.6.3 Dictionary design with graph embedding constraints -- 1.6.4 Kernel sparse methods --
505 8 _a2. Sparse representations -- 2.1 The sparsity regularization -- 2.1.1 Other sparsity regularizations -- 2.1.2 Non-negative sparse representations -- 2.2 Geometrical interpretation -- 2.3 Uniqueness of l0 and its equivalence to the l1 solution -- 2.3.1 Phase transitions -- 2.4 Numerical methods for sparse coding -- 2.4.1 Optimality conditions -- 2.4.2 Basis pursuit -- 2.4.3 Greedy pursuit methods -- 2.4.4 Feature-sign search -- 2.4.5 Iterated shrinkage methods --
505 8 _a3. Dictionary learning: theory and algorithms -- 3.1 Dictionary learning and clustering -- 3.1.1 Clustering procedures -- 3.1.2 Probabilistic formulation -- 3.2 Learning algorithms -- 3.2.1 Method of optimal directions -- 3.2.2 K-SVD -- 3.2.3 Multilevel dictionaries -- 3.2.4 Online dictionary learning -- 3.2.5 Learning structured sparse models -- 3.2.6 Sparse coding using examples -- 3.3 Stability and generalizability of learned dictionaries -- 3.3.1 Empirical risk minimization -- 3.3.2 An example case: multilevel dictionary learning --
505 8 _a4. Compressed sensing -- 4.1 Measurement matrix design -- 4.1.1 The restricted isometry property -- 4.1.2 Geometric interpretation -- 4.1.3 Optimized measurements -- 4.2 Compressive sensing of natural images -- 4.3 Video compressive sensing -- 4.3.1 Frame-by-frame compressive recovery -- 4.3.2 Model-based video compressive sensing -- 4.3.3 Direct feature extraction from compressed videos --
505 8 _a5. Sparse models in recognition -- 5.1 A simple classification setup -- 5.2 Discriminative dictionary learning -- 5.3 Sparse-coding-based subspace identification -- 5.4 Using unlabeled data in supervised learning -- 5.5 Generalizing spatial pyramids -- 5.5.1 Supervised dictionary optimization -- 5.6 Locality in sparse models -- 5.6.1 Local sparse coding -- 5.6.2 Dictionary design -- 5.7 Incorporating graph embedding constraints -- 5.7.1 Laplacian sparse coding -- 5.7.2 Local discriminant sparse coding -- 5.8 Kernel methods in sparse coding -- 5.8.1 Kernel sparse representations -- 5.8.2 Kernel dictionaries in representation and discrimination -- 5.8.3 Combining diverse features -- 5.8.4 Application: tumor identification --
505 8 _aBibliography -- Authors' biographies.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _aImage understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on May 20, 2014).
650 0 _aImage processing
_xDigital techniques
_xMathematics.
650 0 _aSparse matrices.
650 0 _aMachine learning.
650 0 _aComputer vision.
653 _asparse representations
653 _anatural images
653 _aimage reconstruction
653 _aimage recovery
653 _aimage classification
653 _adictionary learning
653 _aclustering
653 _acompressed sensing
653 _akernel methods
653 _agraph embedding
700 1 _aRamamurthy, Karthikeyan Natesan.,
_eauthor.
700 1 _aTuraga, Pavan.,
_eauthor.
700 1 _aSpanias, Andreas.,
_eauthor.
776 0 8 _iPrint version:
_z9781627053594
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on image, video, and multimedia processing ;
_v# 15.
_x1559-8144
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6828191
856 4 0 _3Abstract with links to full text
_uhttp://dx.doi.org/10.2200/S00563ED1V01Y201401IVM015
999 _c562067
_d562067