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.
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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
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)
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)
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)
Choi, H., Baraniuk, R.G.: Multi-scale Image Segmentation Using Wavelet-Domain Hidden Markov Models. IEEE Trans. on Image Processing 10, 1309–1321 (2001)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Krishnapuram, R., Killer, J.M.: A Possibilistic Approach to Clustering. IEEE Trans. on Fuzzy System 1, 98–110 (1993)
Brodatz, P.: Textures: A Photographic Album for Artists & Designers. Dover Publications, Inc., New York (1966)
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Sun, Q., Hou, B., Jiao, Lc. (2005). Automatic Texture Segmentation Based on Wavelet-Domain Hidden Markov Tree. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_49
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DOI: https://doi.org/10.1007/11578079_49
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