Coverart for item
The Resource Image understanding using sparse representations, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias, (electronic book)

Image understanding using sparse representations, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias, (electronic book)

Label
Image understanding using sparse representations
Title
Image understanding using sparse representations
Statement of responsibility
Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias
Creator
Contributor
Author
Subject
Language
eng
Summary
Image 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
Cataloging source
CaBNVSL
http://library.link/vocab/creatorName
Thiagarajan, Jayaraman Jayaraman
Dewey number
006.6
Illustrations
illustrations
Index
no index present
LC call number
TA1637.5
LC item number
.T455 2014
Literary form
non fiction
Nature of contents
  • dictionaries
  • abstracts summaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Ramamurthy, Karthikeyan Natesan.
  • Turaga, Pavan.
  • Spanias, Andreas.
http://library.link/vocab/subjectName
  • Image processing
  • Sparse matrices
  • Machine learning
  • Computer vision
Target audience
  • adult
  • specialized
Label
Image understanding using sparse representations, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias, (electronic book)
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 91-104)
Color
multicolored
Contents
  • 1. 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 --
  • 2. 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 --
  • 3. 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 --
  • 4. 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 --
  • 5. 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 --
  • Bibliography -- Authors' biographies
Control code
201401IVM015
Dimensions
unknown
Extent
1 PDF (xi, 106 pages)
File format
multiple file formats
Form of item
online
Isbn
9781627053594
Other control number
10.2200/S00563ED1V01Y201401IVM015
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader
Label
Image understanding using sparse representations, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Pavan Turaga, Andreas Spanias, (electronic book)
Publication
Bibliography note
Includes bibliographical references (pages 91-104)
Color
multicolored
Contents
  • 1. 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 --
  • 2. 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 --
  • 3. 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 --
  • 4. 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 --
  • 5. 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 --
  • Bibliography -- Authors' biographies
Control code
201401IVM015
Dimensions
unknown
Extent
1 PDF (xi, 106 pages)
File format
multiple file formats
Form of item
online
Isbn
9781627053594
Other control number
10.2200/S00563ED1V01Y201401IVM015
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System details
System requirements: Adobe Acrobat Reader

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