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Contourlet HMT model with directional feature

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Abstract

According to Shanno’s information theory, the directional feature of texture is defined as the value of directional variable when an image signal attains a singularity of random distribution. In terms of this definition, we calculate the texture’s directional features using Tamura’s method and study the directional probability distribution of Contourlet coefficients. Then we find that the directional features tend to be conveyed across parent and child subbands. Based on this conclusion, we establish a novel probability distribution model of hidden direction variables under the condition of hidden state variable’s distribution, named Contourlet HMT model with directional feature. The structure and training method of the model are presented as well. Moreover, an unsupervised context-based image segmentation algorithm is proposed on the basis of the proposed model. Its effectiveness is verified via extensive experiments carried out on several synthesized images and remote sensing images.

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References

  1. Crouse M S, Nowak R D, Baraniuk R G. Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans Signal Process, 1998, 46: 886–902

    Article  MathSciNet  Google Scholar 

  2. Do M N, Vetterli M. Contourlets: a new directional multiresolution image representation. Signal Syst Comput, 2002, 1: 497–501

    Google Scholar 

  3. Fan G L, Xia X G. A joint multi-context and multi-scale approach to Beyesian image segmentation. IEEE Trans Geosci Remote Sens, 2001, 39: 2680–2688

    Article  Google Scholar 

  4. Jiao L C, Hou B, Xu J. Image segmentation using Contourlet domain hidden Markov model based on parametric initialization. SciencePaper Online, 2009. 1–8

  5. Chen G Z, Liu X Z. Contourlet-based despeckling for SAR image using hidden Markov tree and Gaussian Markov models. In: 1st Asian and Pacific Conference on Synthetic Aperture Radar (APSAR 2007), Huangshan, 2007. 784–787

  6. Long Z L, Nicolas H Y. Statistical image modeling in the Contourlet domain using contextual hidden Markov models. Signal Process, 2009, 89: 946–951

    Article  MATH  Google Scholar 

  7. Gopalan C, Manjula D. Contourlet based approach for text identification and extraction from heterogeneous textual images. Int J Comput Sci Eng, 2008, 2: 202–211

    Google Scholar 

  8. Xin F F, Jiao L C, Wan H L. Unsupervised image segmentation based on nonsubsampled Contourlet hidden Markov trees model. In: 2nd Asian-Pacific Conference on Synthetic Aperture Radar (APSAR 2009), Xi’an, 2009. 485–488

  9. Wu Y, Li M, Zong H T, et al. Fusion segmentation algorithm for SAR images based on HMT in Contourlet domain and D-S theory of evidence. In: The 4th IEEE Conference on Industrial Electronics and Applications (ICIEA 2009). LNCS 5593. Berlin: Springer-Verlag, 2009. 937–951

    Google Scholar 

  10. Wu Y, Xiao P, Zong H T, et al. Fusion segmentation algorithm for SAR images based on the persistence and clustering in the Contourlet domain. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, 2009. 402–406

  11. Po D D Y, Do M N. Directional multiscale modeling of images using the contourlet transform. In: IEEE Workshop on Statistical Signal Processing, St. Louis, 2003. 262–265

  12. Tamura H, Mori S, Yamawaki T. Texture features corresponding to visual perception. IEEE Trans Syst Man Cybern, 1978, 8: 460–473

    Article  Google Scholar 

  13. Ling P. Research on texture classification in remote sensing image with wavelet-based hidden Markov tree models. Doctoral Dissertation. Beijing: Institute of Chinese Academy of Sciences, 2005

    Google Scholar 

  14. Ye Z, Lu C C. Wavelet-based unsupervised SAR image segmentation using hidden Markov tree models. In: Proceedings of the 16th International Conference on Pattern Recognition(ICPR’02), Quebec, 2002. 729–732

  15. Choi H, Baraniuk R G. Multiscale image segmentation using wavelet-domain hidden Markov models. IEEE Trans Image Process, 2001, 10: 1309–1321

    Article  MathSciNet  Google Scholar 

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Correspondence to XiangHai Wang.

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Wang, X., Chen, M., Song, C. et al. Contourlet HMT model with directional feature. Sci. China Inf. Sci. 55, 1563–1578 (2012). https://doi.org/10.1007/s11432-012-4609-4

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  • DOI: https://doi.org/10.1007/s11432-012-4609-4

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