Skip to main content
SpringerLink
Log in

Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

References

  1. Bagon, S. (2006). Matlab wrapper for graph cuts.

  2. Chen, J., Pappas, T., Mojsilovic, A., & Rogowitz, B. (2005). Adaptive perceptual color-texture image segmentation. IEEE Transactions on Image Processing, 14(10), 1524–1536.

    Article  Google Scholar 

  3. Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603–619.

    Article  Google Scholar 

  4. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 1, (pp. 886–893). San Diego, USA.

  5. Deng, Y., & Manjunath, B. (2001). Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8), 800–810.

    Article  Google Scholar 

  6. Devaux, J.C., Gouton, P., & Truchetet, F. (2001). Karhunen-loe’ve transform applied to region-based segmentation of color aerial images. Optical Engineering, 40(7), 1302–1308.

    Article  Google Scholar 

  7. Donoser, M., Urschler, M., Hirzer, M., & Bischof, H. (2009). Saliency driven total variation segmentation. In Proceeding IEEE International Conference on Computer Vision (pp. 817–824). Kyoto: IEEE.

    Google Scholar 

  8. Fan, J., Yau, D.K., Elmagarmid, A.K., & Aref, W.G. (2001). Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing, 10(10), 1454–1466.

    Article  MATH  Google Scholar 

  9. Fondón, I., Serrano, C., & Acha, B. (2006). Color-texture image segmentation based on multistep region growing. Optical Engineering, 45(5), 057,002–057,002.

    Article  Google Scholar 

  10. Freixenet, J., Muñoz, X., Raba, D., Martí, J., & Cufí, X. (2002). Yet another survey on image segmentation: Region and boundary information integration. In Proceeding the 7th European Conference on Computer Vision-Part III, vol. 2352 (pp. 408–422). Copenhagen: Springer.

    Google Scholar 

  11. Fulkerson, B., Vedaldi, A., & Soatto, S. (2009). Class segmentation and object localization with superpixel neighborhoods. In Proceedings IEEE International Conference on Computer Vision (pp. 670–677): IEEE.

  12. Geman, S., & Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6(6), 721–741.

    Article  MATH  Google Scholar 

  13. Haralick, R.M., Shanmugam, K., & Dinstein, I.H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3(6), 610–621.

    Article  Google Scholar 

  14. Heiler, M., & Schnörr, C. (2005). Natural image statistics for natural image segmentation. International Journal of Computer Vision, 63(1), 5–19.

    Article  Google Scholar 

  15. Hill, P.R., Canagarajah, C.N., & Bull, D.R. (2003). Image segmentation using a texture gradient based watershed transform. IEEE Transactions on Image Processing, 12(12), 1618–1633.

    Article  MathSciNet  Google Scholar 

  16. Hoiem, D., Efros, A.A., & Hebert, M. (2007). Recovering surface layout from an image. International Journal of Computer Vision, 75(1), 151–172.

    Article  MATH  Google Scholar 

  17. Ilea, D.E., & Whelan, P.F. (2008). Ctexan adaptive unsupervised segmentation algorithm based on color-texture coherence. IEEE Transactions on Image Processing, 17(10), 1926–1939.

    Article  MathSciNet  Google Scholar 

  18. Ilea, D.E., & Whelan, P.F. (2011). Image segmentation based on the integration of colour–texture descriptorsa review. Pattern Recognition, 44(10), 2479–2501.

    Article  MATH  Google Scholar 

  19. Jain, A.K., & Farrokhnia, F. (1990). Unsupervised texture segmentation using gabor filters. In IEEE International Conference on Systems, Man and Cybernetics (pp. 14–19).

  20. Kato, Z., & Pong, T.C. (2006). A markov random field image segmentation model for color textured images. Image and Vision Computing, 24(10), 1103–1114.

    Article  Google Scholar 

  21. Kolmogorov, V., & Zabin, R. (2004). What energy functions can be minimized via graph cuts?. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2), 147–159.

    Article  Google Scholar 

  22. Lee, H., & Kim, C. (2014). Blurred image region detection and segmentation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (pp. 4427–4431). Paris, France.

  23. Liu, C., Yuen, J., & Torralba, A. (2011). Nonparametric scene parsing via label transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12), 2368–2382.

    Article  Google Scholar 

  24. Luccheseyz, L., & Mitray, S. (2001). Color image segmentation: A state-of-the-art survey. Proceedings of the Indian National Science Academy (INSA-A), 67(2), 207–221.

    Google Scholar 

  25. Ma, Y., Derksen, H., Hong, W., & Wright, J. (2007). Segmentation of multivariate mixed data via lossy data coding and compression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9), 1546–1562.

    Article  Google Scholar 

  26. Malisiewicz, T., & Efros, A.A. (2008). Recognition by association via learning per-exemplar distances. In Proceeding IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1–8).

  27. Martin, D.R., Malik, J., & Patterson, D. (2003). An Empirical Approach to Grouping and Segmentaqtion. Computer Science Division, University of California.

  28. Mobahi, H., Rao, S., Yang, A., Sastry, S., & Ma, Y. (2011). Segmentation of natural images by texture and boundary compression. International Journal of Computer Vision, 95(1), 86–98.

    Article  Google Scholar 

  29. Ojala, T., & Pietikäinen, M. (1999). Unsupervised texture segmentation using feature distributions. Pattern Recognition, 32(3), 477–486.

    Article  Google Scholar 

  30. Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

    Article  MATH  Google Scholar 

  31. Panagiotakis, C., Grinias, I., & Tziritas, G. (2011). Natural image segmentation based on tree equipartition, bayesian flooding and region merging. IEEE Transactions on Image Processing, 20(8), 2276–2287.

    Article  MathSciNet  Google Scholar 

  32. Rand, W.M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association, 66(336), 846–850.

    Article  Google Scholar 

  33. Rao, S.R., Mobahi, H., Yang, A.Y., Sastry, S.S., & Ma, Y. (2010). Natural image segmentation with adaptive texture and boundary encoding. In The Tenth Asian Conference on Computer Vision (pp. 135–146).

  34. Scarpa, G., Gaetano, R., Haindl, M., & Zerubia, J. (2009). Hierarchical multiple markov chain model for unsupervised texture segmentation. IEEE Transactions on Image Processing, 18(8), 1830–1843.

    Article  MathSciNet  Google Scholar 

  35. Vu, N.B.S. (2008). Image Segmentation with Semantic Priors: A Graph Cut Approach. Ph.D. Thesis. Santa Barbara: University of California.

    Google Scholar 

  36. Wang, J., Jia, Y., Hua, X.S., Zhang, C., & Quan, L. (2008). Normalized tree partitioning for image segmentation. In Proceedings IEEE International Conference on Computer Vision and Pattern Recognition (pp. 1–8).

  37. Wang, X., & Zhang, X.P. (2009). A new localized superpixel markov random field for image segmentation. In IEEE International Conference on Multimedia and Expo (pp. 642–645).

  38. Yang, A.Y., Wright, J., Ma, Y., & Sastry, S.S. (2008). Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding, 110(2), 212–225.

    Article  Google Scholar 

Download references

Acknowledgments

This project was funded by Samsung Electronics Co., Ltd.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology(KAIST), Daejeon, 305-701, Korea

    Na Kyoung O & Changick Kim

Authors
  1. Na Kyoung O
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changick Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changick Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O, N.K., Kim, C. Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field. J Sign Process Syst 83, 423–436 (2016). https://doi.org/10.1007/s11265-015-1030-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-015-1030-4

Keywords

Search

Navigation