000 | 06339nam a2200805 i 4500 | ||
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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 |
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020 |
_z9781627053594 _qpaperback |
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024 | 7 |
_a10.2200/S00563ED1V01Y201401IVM015 _2doi |
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035 | _a(CaBNVSL)swl00403380 | ||
035 | _a(OCoLC)880357632 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aTA1637.5 _b.T455 2014 |
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082 | 0 | 4 |
_a006.6 _223 |
090 |
_a _bMoCl _e201401IVM015 |
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100 | 1 |
_aThiagarajan, Jayaraman Jayaraman., _eauthor. |
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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. |
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300 |
_a1 PDF (xi, 106 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 |
_aSynthesis lectures on image, video, and multimedia processing, _x1559-8144 ; _v# 15 |
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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. |
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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. |
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700 | 1 |
_aTuraga, Pavan., _eauthor. |
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700 | 1 |
_aSpanias, Andreas., _eauthor. |
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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 |
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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 |