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Texture Image Segmentation Based on Stationary Directionlet Domain Probabilistic Graphical Model

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E-Learning and Games (Edutainment 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11462))

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Abstract

In this paper, a stationary directionlet (SD) domain probabilistic graphical model (PGM) for texture image segmentation is proposed. Hidden markov chain (HMC) is a good tool to capture the persistence and clustering properties of the coefficients of SD transform. The homogeneous property of texture image is described by markov random field (MRF). Combining HMC and MRF in SD domain result in SDPGM. Image segmentation based on SDPGM, which is denoted as SDPGMseg, involves inferring the maximum a posterior (MAP) solution to class labels on the coefficients of SD transform. The segmentation result can be obtained by minimizing an energy function. Experiment results show that SDPGMseg can obtain better performance especially in homogeneous regions and boundaries of different regions.

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References

  1. Maninis, K.K., Pont-Tuset, J., Arbelaez, P., Gool, L.V.: Convolutional oriented boundaries: from image segmentation to high-level tasks. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2017)

    Google Scholar 

  2. Qian, P., et al.: Knowledge-leveraged transfer fuzzy c-means for texture image segmentation with self-adaptive cluster prototype matching. Knowl.-Based Syst. 130, 33–50 (2017)

    Article  Google Scholar 

  3. Min, H., et al.: An intensity-texture model based level set method for image segmentation. Pattern Recogn. 48(4), 1547–1562 (2015)

    Article  Google Scholar 

  4. Devi, C.N., Chandrasekharan, A., Sundararaman, V., Alex, Z.C.: Neonatal brain MRI segmentation: a review. Comput. Biol. Med. 64, 163–178 (2015)

    Article  Google Scholar 

  5. Masood, S., Sharif, M., Masood, A., Yasmin, M., Raza, M.: A survey on medical image segmentation. Curr. Med. Imaging Rev. 11(1), 3–14 (2015)

    Article  Google Scholar 

  6. Shankar, T., Yamuna, G., Suman, G.: Segmentation of natural colour image based on colour-texture features. In: 2013 International Conference on Communications and Signal Processing (ICCSP), pp. 455–459. IEEE (2013)

    Google Scholar 

  7. Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans. Sig. Process. 46(4), 886–902 (1998)

    Article  MathSciNet  Google Scholar 

  8. Choi, H., Baraniuk, R.G.: Multiscale image segmentation using wavelet-domain hidden Markov models. IEEE Trans. Image Process. 10(9), 1309–1321 (2001)

    Article  MathSciNet  Google Scholar 

  9. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(84), 191–203 (1984)

    Article  Google Scholar 

  10. Bai, J., Zhao, J., Jiao, L.: Image segmentation using directionlet-domain hidden Markov tree models. In: 2011 IEEE CIE International Conference on Radar (Radar), vol. 2, pp. 1615–1618. IEEE (2011)

    Google Scholar 

  11. Sha, Y., Cong, L., Sun, Q., Jiao, L.: Unsupervised image segmentation using contourlet domain hidden Markov trees model. In: Kamel, M., Campilho, A. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 32–39. Springer, Heidelberg (2005). https://doi.org/10.1007/11559573_5

    Chapter  Google Scholar 

  12. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)

    Article  Google Scholar 

  13. Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. Int. J. Comput. Vis. 96(1), 1–27 (2012)

    Article  MathSciNet  Google Scholar 

  14. Brodatz, P.: Textures: a photographic album for artists and designers. Department of the University of Central Florida

    Google Scholar 

Download references

Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (No. U1610124, 61772530 and 61572505), and the National Key Research and Development Plan (No. 2016YFC0600908), and the National Natural Science Foundation of Jiangsu Province (No. BK20171192).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou, 221116, Jiangsu, China

    Zhenguo Gao, Shixiong Xia & Jiaqi Zhao

Authors
  1. Zhenguo Gao
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  2. Shixiong Xia
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  3. Jiaqi Zhao
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Corresponding author

Correspondence to Shixiong Xia .

Editor information

Editors and Affiliations

  1. Liverpool John Moores University, Liverpool, UK

    Abdennour El Rhalibi

  2. Hangzhou Normal University, Hangzhou, China

    Zhigeng Pan

  3. Xi’an University of Technology, Xi’an, China

    Haiyan Jin

  4. Hangzhou Normal University, Hangzhou, China

    Dandan Ding

  5. Pontificia Universidad Javeriana, Cali, Colombia

    Andres A. Navarro-Newball

  6. Xi’an University of Technology, Xi’an, China

    Yinghui Wang

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Gao, Z., Xia, S., Zhao, J. (2019). Texture Image Segmentation Based on Stationary Directionlet Domain Probabilistic Graphical Model. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_55

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  • DOI: https://doi.org/10.1007/978-3-030-23712-7_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23711-0

  • Online ISBN: 978-3-030-23712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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