Share Email Print

Proceedings Paper

Learning classes of efficient codes
Author(s): Te-Won Lee; Michael S. Lewicki
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We are interested in leaning efficient codes to represent classes of different images. The image classes are modeled using an ICA mixture model that assumes that the data was generated by several mutually exclusive data classes whose components are a mixture of non-Gaussian sources. The parameters of the model can be adapted using an approximate expectation maximization approach to maximize the data likelihood. We demonstrate that this method can learn classes of efficient codes to represent images that contain a variety of different structures. The learned codes can be used for image compression, de-noising and classification tasks. Compared to standard image coding methods, the ICA mixture model gives better encoding results because the codes are adapted to the structure of the data.

Paper Details

Date Published: 4 December 2000
PDF: 6 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408633
Show Author Affiliations
Te-Won Lee, Univ. of California/San Diego (United States)
Michael S. Lewicki, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 4119:
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?