Paper
4 December 2000 Rotation-invariant texture retrieval using steerable wavelet-domain hidden Markov models
Minh N. Do, Aurelie C. Lozano, Martin Vetterli
Author Affiliations +
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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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Cited by 8 scholarly publications.
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KEYWORDS
Wavelets

Databases

Data modeling

Matrices

Image retrieval

Expectation maximization algorithms

Statistical analysis

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