Parametric Gaussianization procedure of wavelet coefficients for texture retrieval

Noureddine Lasmar, Youssef Stitou, Soufiane Jouini, Yannick Berthoumieu, Mohamed Najim

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

4 Scopus citations

Abstract

In this paper, we deal with the problem of feature extraction in content-based image retrieval (CBIR) using statistical approach. A Gaussianization procedure based on parametric density assumptions of steerable pyramid coefficients is proposed. The extraction method of features including the Gaussianization step allows us to limit the order of the statistical model used to characterize the image textures. The performances of the proposed method are analyzed on a database of texture images and compared with the performances of other texture features proposed in previous works.

Original languageBritish English
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages749-752
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 31 Mar 20084 Apr 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period31/03/084/04/08

Keywords

  • Gaussianization procedure
  • Generalized gaussian distribution
  • Image texture analysis
  • Information retrieval
  • Steerable pyramid

Fingerprint

Dive into the research topics of 'Parametric Gaussianization procedure of wavelet coefficients for texture retrieval'. Together they form a unique fingerprint.

Cite this