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

Minh N. Do; Aurelie C. Lozano; Martin Vetterli

doi:10.1117/12.408611

4 December 2000 Rotation-invariant texture retrieval using steerable wavelet-domain hidden Markov models

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

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408611
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

Citation Download Citation

Minh N. Do, Aurelie C. Lozano, and Martin Vetterli "Rotation-invariant texture retrieval using steerable wavelet-domain hidden Markov models", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408611

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