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

We present a new statistical model for characterizing texture images based on wavelet-domain hidden Markov models. With a small number of parameters, the new model captures both the subband marginal distributions and the dependencies across scales and orientations of the wavelet descriptors. Applying to the steerable pyramid, once it is trained for an input texture image, the model can be easily steered to characterize that texture at any other orientation. Furthermore, after a diagonalization operation, we obtain a rotation-invariant model of the texture image. We also propose a fast algorithm to approximate the Kullback

• Type: Journal
• Year: 2002
• Where: TMM
• Authors: Minh N. Do, Martin Vetterli
• Keywords: Hidden Markov Models, Texture Image, TMM 2002, Wavelet-domain Hidden Markov