Unsupervised Texture Classification and Segmentation
An unsupervised classification algorithm is derived
by modeling observed data as a mixture of several mutually
exclusive classes that are each described by linear combinations of
independent non-Gaussian densities. The algorithm estimates the
data density in each class by using parametric nonlinear functions
that fit to the non-Gaussian structure of the data. This improves
classification accuracy compared with standard Gaussian mixture
models. When applied to textures, the algorithm can learn basis
functions for images that capture the statistically significant structure
intrinsic in the images. We apply this technique to the problem of
unsupervised texture classification and segmentation.
[1] R.W.Buccigrossi, E.P.Simoncelli, "Image compression via joint
statistical characterisation in the wavelet domain," IEEE Trans. Image
Processing, Vol.8, Dec. 1999, pp.1688-1701.
[2] J.-F. Cardos, B.Laheld, "Equivariant adaptive source separation", IEEE.
Trans. Signal Processing", Vol. 45, Feb. 1996, pp.434-444.
[3] R.O.Duda and P.E. Hart, Pattern Classification and Scene Analysis.
New York: Wiley, 1973.
[4] Y.-W.Wang, Y.-F.Wang, Y. Xue, W.Gao, "A new algorithm for
remotely sensed image classification and segmentation", GeoScience
and Remote Sensing Symposium 2003, IGARSS-03 Proceedings, Vol.6,
July 2003, pp.3534-3536.
[5] Unser. M, "Texture classification and segmentation using wavelet
frames," IEEE Trans. Image Processing., Vol.4, Issue 11, Nov.
1995,pp.1549-1560.
[6] Charalampidis.D, Kasparis.T, "Wavelet-based rotational invariant
roughness features for texture classification and segmentation," IEEE
Trans.Image Processing., Vol. 11, Issue .8, Aug. 2002, pp.825-837.
[7] Kaplan L.M., "Extended fractal analysis for texture classification and
segmentation", IEEE Trans. Image Processing, Vol.8, Issue 11, Nov
1999, pp.1572-1585.
[8] Arof.H, Deravi.F, "Circular neighbourhood and 1-D DFT features for
texture classification and segmentation," IEEE Proc. Vision, Image and
Signal Processing, Vol.145, Issue 3, June 1998, pp.167-172.
[9] Tan T.N., Constantinides A.G., "Texture analysis based on a human
visual system", Acoustics, Speech and Signal Processing, ICASSP 90, 3-
6, April, 1990, Vol.4, pp.2137-2140.
[10] Lueng. M., Peterson A.M., "Multiple channel neural network model for
texture classification and segmentation," Acoustics Speech and Signal
Processing ICASSP 91, 14-17 April 1991, Vol.4,pp.2677-2680.
[11] Mengyang Liao, J.Qin, Yanni Tan, "Texture classification and
segmentation using autoregressive models," Computer-based Medical
Systems, Fifth Annual IEEE Symposiums, 14-17 June, 1992, pp.398-
401.
[12] Patel .D, Stonham .T.J.., "Texture image classification and segmentation
using rank-order clustering," Pattern Recognition, Vol.3, Eleventh IAPR
International Conference, 30 Aug-3 Sep, 1992, pp.92-95.
[13] Jiang Wen, You Zhiseng, Li Hui, "Segment the metallograph images
using Gabor filter", Speech, Image Processing and Neural Networks,
Proceedings ISSIPNN-94, 1994 International Symposium, 13-16 Apr,
1994, Vol.1, pp.25-28.
[14] Oja.E, Valkealathi .K, "Compressing higher-order co-occurrences for
texture analysis using self-organising maps" in International
Conference on Neural Networks,,27 Nov-1 Dec, 1995, Vol.2. pp.1160-
1164.
[15] Gambotto.J, Gueguen.C,, "A multidimensional modeling approach to
texture classification and segmentation," Acoustics, Speech and Image
Processing, IEEE Int. Conf. on ICASSP-79 Vol.4., pp. 962-966.
[16] Arrowsmith.M.J., Varley.M.R., Picton. P.D., Heys. J.D.,, "Hybrid neural
network system for texture analysis", Image Processing and its
Applications 1999, Seventh International Conference (pub.No.465), 13-
15 July 1999,Vol..1, pp.339-343.
[17] Claude.I, Smolarz.Z.A, "A new textured image segmentation algorithm
by autoregressive modeling and multiscale block classification", Image
Processing and its Applications 1997, Sixth.International Conference,
14-17 July 1997, Vol.2, pp. 586-590.
[1] R.W.Buccigrossi, E.P.Simoncelli, "Image compression via joint
statistical characterisation in the wavelet domain," IEEE Trans. Image
Processing, Vol.8, Dec. 1999, pp.1688-1701.
[2] J.-F. Cardos, B.Laheld, "Equivariant adaptive source separation", IEEE.
Trans. Signal Processing", Vol. 45, Feb. 1996, pp.434-444.
[3] R.O.Duda and P.E. Hart, Pattern Classification and Scene Analysis.
New York: Wiley, 1973.
[4] Y.-W.Wang, Y.-F.Wang, Y. Xue, W.Gao, "A new algorithm for
remotely sensed image classification and segmentation", GeoScience
and Remote Sensing Symposium 2003, IGARSS-03 Proceedings, Vol.6,
July 2003, pp.3534-3536.
[5] Unser. M, "Texture classification and segmentation using wavelet
frames," IEEE Trans. Image Processing., Vol.4, Issue 11, Nov.
1995,pp.1549-1560.
[6] Charalampidis.D, Kasparis.T, "Wavelet-based rotational invariant
roughness features for texture classification and segmentation," IEEE
Trans.Image Processing., Vol. 11, Issue .8, Aug. 2002, pp.825-837.
[7] Kaplan L.M., "Extended fractal analysis for texture classification and
segmentation", IEEE Trans. Image Processing, Vol.8, Issue 11, Nov
1999, pp.1572-1585.
[8] Arof.H, Deravi.F, "Circular neighbourhood and 1-D DFT features for
texture classification and segmentation," IEEE Proc. Vision, Image and
Signal Processing, Vol.145, Issue 3, June 1998, pp.167-172.
[9] Tan T.N., Constantinides A.G., "Texture analysis based on a human
visual system", Acoustics, Speech and Signal Processing, ICASSP 90, 3-
6, April, 1990, Vol.4, pp.2137-2140.
[10] Lueng. M., Peterson A.M., "Multiple channel neural network model for
texture classification and segmentation," Acoustics Speech and Signal
Processing ICASSP 91, 14-17 April 1991, Vol.4,pp.2677-2680.
[11] Mengyang Liao, J.Qin, Yanni Tan, "Texture classification and
segmentation using autoregressive models," Computer-based Medical
Systems, Fifth Annual IEEE Symposiums, 14-17 June, 1992, pp.398-
401.
[12] Patel .D, Stonham .T.J.., "Texture image classification and segmentation
using rank-order clustering," Pattern Recognition, Vol.3, Eleventh IAPR
International Conference, 30 Aug-3 Sep, 1992, pp.92-95.
[13] Jiang Wen, You Zhiseng, Li Hui, "Segment the metallograph images
using Gabor filter", Speech, Image Processing and Neural Networks,
Proceedings ISSIPNN-94, 1994 International Symposium, 13-16 Apr,
1994, Vol.1, pp.25-28.
[14] Oja.E, Valkealathi .K, "Compressing higher-order co-occurrences for
texture analysis using self-organising maps" in International
Conference on Neural Networks,,27 Nov-1 Dec, 1995, Vol.2. pp.1160-
1164.
[15] Gambotto.J, Gueguen.C,, "A multidimensional modeling approach to
texture classification and segmentation," Acoustics, Speech and Image
Processing, IEEE Int. Conf. on ICASSP-79 Vol.4., pp. 962-966.
[16] Arrowsmith.M.J., Varley.M.R., Picton. P.D., Heys. J.D.,, "Hybrid neural
network system for texture analysis", Image Processing and its
Applications 1999, Seventh International Conference (pub.No.465), 13-
15 July 1999,Vol..1, pp.339-343.
[17] Claude.I, Smolarz.Z.A, "A new textured image segmentation algorithm
by autoregressive modeling and multiscale block classification", Image
Processing and its Applications 1997, Sixth.International Conference,
14-17 July 1997, Vol.2, pp. 586-590.
@article{"International Journal of Information, Control and Computer Sciences:53501", author = "V.P.Subramanyam Rallabandi and S.K.Sett", title = "Unsupervised Texture Classification and Segmentation", abstract = "An unsupervised classification algorithm is derived
by modeling observed data as a mixture of several mutually
exclusive classes that are each described by linear combinations of
independent non-Gaussian densities. The algorithm estimates the
data density in each class by using parametric nonlinear functions
that fit to the non-Gaussian structure of the data. This improves
classification accuracy compared with standard Gaussian mixture
models. When applied to textures, the algorithm can learn basis
functions for images that capture the statistically significant structure
intrinsic in the images. We apply this technique to the problem of
unsupervised texture classification and segmentation.", keywords = "Gaussian Mixture Model, Independent Component
Analysis, Segmentation, Unsupervised Classification.", volume = "1", number = "11", pages = "3439-4", }
