Texture Image Segmentation Based on MRMRF in Contourlet Domain

Abstract:

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This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

Info:

Periodical:

Advanced Materials Research (Volumes 532-533)

Edited by:

Suozhang Cai and Mingli Li

Pages:

732-737

DOI:

10.4028/www.scientific.net/AMR.532-533.732

Citation:

X. J. Wang and X. F. Zhao, "Texture Image Segmentation Based on MRMRF in Contourlet Domain", Advanced Materials Research, Vols. 532-533, pp. 732-737, 2012

Online since:

June 2012

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Price:

$38.00

[1] Lei Wang, Jun Liu, Texture Classification Using Multiresolution Markov Random Field Models, Pattern Recognition Letters, vol. 20, pp.171-182, (1999).

DOI: 10.1016/s0167-8655(98)00129-9

[2] Hideki Noda, Mahdad N. Shirazi, and Eiji Kawaguchi, MRF-based texture segmentation using wavelet decomposed images, Pattern Recognition, vol. 35, pp.771-782, (2002).

DOI: 10.1016/s0031-3203(01)00077-2

[3] F.A. Tab, G. Naghty, and A. Merins, Scalable Multiresolution Color Image Segmentation, Signal Processing, vol. 86, pp.1670-1687, (2006).

DOI: 10.1016/j.sigpro.2005.09.016

[4] L. Zheng, J.C. Liu, and W. Smith, Object-based Image Segmentation Using Dwt/Rdwt Multiresolution Markov Random Field, in 1999 IEEE Conference on Acoustics, Speech, and Signal Processing, Phonix, America, pp.3485-3488, March 15-19, (1999).

DOI: 10.1109/icassp.1999.757593

[5] Thrasyvoulos N. Pappas, An Adaptive Clustering Algorithm for Image Segmentation, IEEE Transactions on Signal Processing, vol. 40, no. 4, pp.901-914, April, (1992).

DOI: 10.1109/78.127962

[6] LIN Li-Yu, ZHANG You-Yan and SUN Tao, etc. The Contourlet Transformation: Applications in Image Processing, The China Press of Science, Beijing, China, (2008).

[7] Krishnamachari, S. and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation,. IEEE Transactions on Image Processing, 1997. 6(2): pp.251-267.

DOI: 10.1109/83.551696

[8] Myunghee Jung, Eui-Jung Yun, and C. -S. Kim. Multiresolution Approach for Texture Segmentation Using MRF Models,. Geoscience and Remote Sensing Symposium, 2005. 2005. 7.

DOI: 10.1109/igarss.2005.1525782

[9] Brodatz P. Textures—A Photographic Album for Artists and Designers, . Dover, New York, USA, (2008).

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