Lossy compression of hyperspectral images using online learning based sparse coding

Irem Ülkü, B. Uʇur Töreyin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

A lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.

Original languageEnglish
Title of host publication2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479979714
DOIs
Publication statusPublished - 13 Jan 2014
Externally publishedYes
Event2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014 - Paris, France
Duration: 1 Nov 20142 Nov 2014

Publication series

Name2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014

Conference

Conference2014 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2014
Country/TerritoryFrance
CityParis
Period1/11/142/11/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Anomaly Detection
  • Hyperspectral Imagery
  • Online Learning
  • Sparse Coding

Fingerprint

Dive into the research topics of 'Lossy compression of hyperspectral images using online learning based sparse coding'. Together they form a unique fingerprint.

Cite this