Shift-invariant sparse representation of images using learned dictionaries

Jayaraman J. Thiagarajan, Karthikeyan N. Ramamurthy, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

Sparse approximations that are evaluated using overcomplete learned dictionaries are useful in many image processing applications such as compression, denoising and feature extraction. Incorporating shift invariance into sparse representation of images can improve sparsity while providing a good approximation. The K-SVD algorithm adapts the dictionary based on a set of training examples, without shift invariance constraints. This paper presents two algorithms for training dictionaries and evaluating shift-invariant sparse representations for image data. One is a modified version of the K-SVD algorithm and the other is a novel graph-based algorithm that adapts the dictionary and computes representations using a low complexity reconstruction procedure.

Original languageEnglish (US)
Title of host publicationProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages145-150
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico
Duration: Oct 16 2008Oct 19 2008

Publication series

NameProceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Other

Other2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
CountryMexico
CityCancun
Period10/16/0810/19/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Electrical and Electronic Engineering

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