Details

Autor(en) / Beteiligte

• Do, M.N.
• Vetterli, M.

Titel

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

Ist Teil von

• IEEE transactions on multimedia, 2002-12, Vol.4 (4), p.517-527

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2002

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• We present a 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. Applied 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-Leibler distance between two wavelet-domain hidden Markov models. We demonstrate the effectiveness of the new texture models in retrieval experiments with large image databases, where significant improvements are shown.