A Similar K-SVD Optimization Algorithm Generalizing the K-Means and the Bayesian tracking

DOI

10.2991/iccsee.2013.427How to use a DOI?

Keywords

Sparese Repressentation, Bayesina Prior, K-SVD, Atom decomposition, Dictionaty.

Abstract

The last decade have seen tremendous improvement in the development of new image information processing and computational tools based on sparse representation. Today, in the information sciences, computer vision and image process-ing, the development of sparse representation algorithms led to convenient tools to transient compressed image (data) rapidly, to remove noise from image, and to get the super-resolution image. In the study of sparse representation of images, overcomplete dictionary is used. It contains prototype image-atoms. In this way, the images are described by sparse linear combinations of theses atoms. In this field has concentrated mainly on the design of a better dictionary. The generalized K-Means algorithm (K-SVD) [1] taught us a very good case. This paper has proposed an optimization algorithm adopting the Bayesian tracking and K-SVD analysis method. We analyze this algorithm and demonstrate its results on image data.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Renjie WU
AU  - S. Kamata
PY  - 2013/03
DA  - 2013/03
TI  - A Similar K-SVD Optimization Algorithm Generalizing the K-Means and the Bayesian tracking
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1707
EP  - 1710
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.427
DO  - 10.2991/iccsee.2013.427
ID  - WU2013/03
ER  -