Rotation invariant texture retrieval using steerable wavelet-domain hidden Markov models

Minh N. Do, Aurélie C. Lozano, Martin Vetterli

Research output: Contribution to journalArticlepeer-review

Abstract

A new statistical model for characterizing texture images based on wavelet-domain hidden Markov models and steerable pyramids is presented. The new model is shown to capture well both the subband marginal distributions and the dependencies across scales and orientations of the wavelet descriptors. Once it is trained for an input texture image, the model can be easily steered to characterize that texture at any other orientations. After a diagonalization operation, one obtains a rotation-invariant description of the texture image. The effectiveness of the new model is demonstrated in large test image databases where significant gains in retrieval performance are shown.

Original languageEnglish (US)
Pages (from-to)274-285
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4119
Issue number1
DOIs
StatePublished - Dec 4 2000
Externally publishedYes

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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