Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 109-132).

1. Introduction -- 1.1 Orthogonal dictionaries in transforms -- 1.2 Dictionaries in clustering algorithms --

2. Fundamental computing tasks in sparse representation -- 2.1 Dictionary-based sparse representation -- 2.2 Sparse representation with matrices -- 2.3 Sparse representation via statistical learning --

3. Dictionary learning algorithms -- 3.1 Reconstructive dictionary learning -- 3.1.1 Learning shift-invariant dictionaries -- 3.1.2 Learning dictionaries in the kernel space -- 3.1.3 Other dictionary learning algorithms -- 3.2 Discriminative dictionary learning -- 3.2.1 Explicit discriminative dictionary learning -- 3.2.2 Implicit discriminative dictionary learning -- 3.3 Joint learning of multiple dictionaries -- 3.3.1 Learning dictionaries from multiple clusters -- 3.3.2 Learning dictionaries from multiple subspaces -- 3.3.3 Learning dictionaries from multiple domains -- 3.3.4 Learning dictionaries with a hierarchy -- 3.4 Online dictionary learning -- 3.5 Statistical dictionary learning --

4. Applications of dictionary learning in visual computing -- 4.1 Signal compression -- 4.1.1 Image compression -- 4.1.2 Face image compression -- 4.1.3 Audio signal compression -- 4.2 Signal recovery -- 4.2.1 Image denoising -- 4.2.2 Image inpainting -- 4.2.3 Image demosaicing -- 4.2.4 Other signal recovery applications -- 4.3 Image super-resolution -- 4.4 Segmentation -- 4.4.1 Image segmentation -- 4.4.2 Background subtraction -- 4.4.3 Blind source separation -- 4.5 Image classification -- 4.6 Saliency detection -- 4.7 Visual tracking --

5. An instructive case study with face recognition -- 5.1 A basic dictionary-based formulation -- 5.2 An improved formulation -- 5.3 Solving the learning problem -- 5.4 Face recognition with the learned dictionary --

Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

INSPEC

Google scholar

Google book search

The 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.

Also available in print.

Title from PDF title page (viewed on May 20, 2015).