Advertisement

Automatic Texture Segmentation Based on Wavelet-Domain Hidden Markov Tree

  • Qiang Sun
  • Biao Hou
  • Li-cheng Jiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

An automatic texture segmentation approach is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited to characterize the texture features of an image, an effective cluster validity index, the ratio of the overlap degree to the separation one between different fuzzy clusters, is used to determine the true number of the textures within an image by solving the minimum of this index in terms of different number of clusters, and the possibilistic C-means (PCM) clustering is performed to extract the training sample data from different textures. In this way, unsupervised segmentation is changed into self-supervised one, and the well-known HMTseg algorithm in the WD-HMT framework is eventually used to produce the final segmentation results, consequently automatic segmentation process is completed. This new approach is applied to segment a variety of composite textured images into distinct homogeneous regions with satisfactory segmentation results demonstrated. Real-world images are also segmented to further justify our approach.

Keywords

Discrete Wavelet Transform Gaussian Mixture Model Wavelet Coefficient Fuzzy Cluster Synthetic Aperture Radar 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-Based Signal Processing Using Hidden Markov Models. IEEE Trans. on Signal Processing 46, 886–902 (1998)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Romberg, J.K., Choi, H., Baraniuk, R.G.: Bayesian Tree-structured Image Modeling Using Wavelet-Domain Hidden Markov Models. IEEE Trans. on Image Processing 10, 1056–1068 (2001)CrossRefGoogle Scholar
  3. 3.
    Fan, G.L., Xia, X.G.: Image Denoising Using Local Contextual Hidden Markov Model in the Wavelet Domain. IEEE Signal Processing Letters 8, 125–128 (2001)CrossRefGoogle Scholar
  4. 4.
    Choi, H., Baraniuk, R.G.: Multi-scale Image Segmentation Using Wavelet-Domain Hidden Markov Models. IEEE Trans. on Image Processing 10, 1309–1321 (2001)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Fan, G.L., Xia, X.G.: A Joint Multi-Context and Multi-Scale Approach to Bayesian Image Segmentation. IEEE Trans. on Geoscience and Remote Sensing 39, 2680–2688 (2001)CrossRefGoogle Scholar
  6. 6.
    Fan, G.L., Xia, X.G.: Wavelet-Based Texture Analysis and Synthesis Using Hidden Markov Models. IEEE Trans. on Circuits and Systems 50, 106–120 (2003)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Do, M.N., Vetterli, M.: Rotation Invariant Texture Characterization and Retrieval Using Steerable Wavelet-Domain Hidden Markov Models. IEEE Trans. on Multimedia 4, 517–527 (2002)CrossRefGoogle Scholar
  8. 8.
    Venkatachalam, V., Choi, H., Baraniuk, R.G.: Multi-scale SAR Image Segmentation Using Wavelet-Domain Hidden Markov Tree Models. In: Proc. of SPIE, vol. 4053, pp. 1605–1611 (2000)Google Scholar
  9. 9.
    Zhen, Y., Lu, C.C.: Wavelet-Based Unsupervised SAR Image Segmentation Using Hidden Markov Tree Models. In: Proc. of International Conference on Pattern Recognition, vol. 2, pp. 729–732 (2002)Google Scholar
  10. 10.
    Song, X.M., Fan, G.L.: Unsupervised Bayesian Image Segmentation Using Wavelet-Domain Hidden Markov Models. In: Proc. of International Conference on Image Processing, vol. 2, pp. 423–426 (2003)Google Scholar
  11. 11.
    Sun, Q., Gou, S.P., Jiao, L.C.: A New Approach to Unsupervised Image Segmentation Based on Wavelet-Domain Hidden Markov Tree Models. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 41–48. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Xu, Q., Yang, J., Ding, S.Y.: Unsupervised Multi-scale Image Segmentation Using Wavelet Domain Hidden Markov Tree. In: Zhang, C., W. Guesgen, H., Yeap, W.-K. (eds.) PRICAI 2004. LNCS (LNAI), vol. 3157, pp. 797–804. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Kim, D.W., Lee, K.H., Lee, D.: On Cluster Validity Index for Estimation of the Optimal Number of Fuzzy Clusters. Pattern Recognition 37, 2009–2025 (2004)CrossRefGoogle Scholar
  14. 14.
    Krishnapuram, R., Killer, J.M.: A Possibilistic Approach to Clustering. IEEE Trans. on Fuzzy System 1, 98–110 (1993)CrossRefGoogle Scholar
  15. 15.
    Brodatz, P.: Textures: A Photographic Album for Artists & Designers. Dover Publications, Inc., New York (1966)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qiang Sun
    • 1
  • Biao Hou
    • 1
  • Li-cheng Jiao
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina

Personalised recommendations