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

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.

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[3]  B. S. Manjunath,et al.  Rotation-invariant texture classification using a complete space-frequency model , 1999, IEEE Trans. Image Process..

[4]  Edward H. Adelson,et al.  Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.

[5]  Richard G. Baraniuk,et al.  Image segmentation using wavelet-domain classification , 1999, Optics & Photonics.

[6]  Justin K. Romberg,et al.  Bayesian tree-structured image modeling using wavelet-domain hidden Markov models , 2001, IEEE Trans. Image Process..

[7]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[8]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[9]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[10]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[11]  William T. Freeman,et al.  Presented at: 2nd Annual IEEE International Conference on Image , 1995 .

[12]  Justin K. Romberg,et al.  Bayesian tree-structured image modeling , 2000, 4th IEEE Southwest Symposium on Image Analysis and Interpretation.

[13]  Pietro Perona,et al.  Rotation invariant texture recognition using a steerable pyramid , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[14]  Wen-Rong Wu,et al.  Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition, and hidden Markov model , 1996, IEEE Trans. Image Process..

[15]  Hyeokho Choi.  Image Segmentation Using Wavelet-domain Classiication , 1999 .

[16]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Shih-Fu Chang,et al.  Transform features for texture classification and discrimination in large image databases , 1994, Proceedings of 1st International Conference on Image Processing.

[18]  Eero P. Simoncelli,et al.  Texture characterization via joint statistics of wavelet coefficient magnitudes , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[19]  A. Kundu,et al.  Rotation and Gray Scale Transform Invariant Texture Identification using Wavelet Decomposition and Hidden Markov Model , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Minh N. Do,et al.  Texture similarity measurement using Kullback-Leibler distance on wavelet subbands , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).