Share Email Print
cover

Proceedings Paper

Rotation-invariant texture retrieval using steerable wavelet-domain hidden Markov models
Author(s): Minh N. Do; Aurelie C. Lozano; Martin Vetterli
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

A new statistical model for characterizing texture images base don 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.

Paper Details

Date Published: 4 December 2000
PDF: 12 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408611
Show Author Affiliations
Minh N. Do, Swiss Federal Institute of Technology Lausanne (Switzerland)
Aurelie C. Lozano, Swiss Federal Institute of Technology Lausanne (Switzerland)
Martin Vetterli, Swiss Federal Institute of Technology Lausanne and Univ. of California/Berkeley (Switzerland)


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