Skip to main content
Springer Nature Link
Log in

Texture segmentation using image decomposition and local self-similarity of different features

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this paper, we propose a new two channels feature space active contours model for texture segmentation by using image decomposition and local self-similarity descriptor of textures. The piece-wise smooth component of image decomposition is regarded as one channel of feature space for segmentation. Defined as a symmetry matrix and a kind of features fusion tool, the local self-similarity descriptor SSM captures the internal geometric layout of local repetitive pattern regions and is computed on different features of textures including oscillatory component of image decomposition, phase congruency and log-Gabor filters responses. A distance map dSSM can measure the similarities between the descriptor of template and the local windows surrounding every pixel on the texture image. And then dSSM is set as another channel of feature space for segmentation. Based on the space, texture segmentation is performed by using active contours and level set technology. In addition, the accuracy of texture boundary localization and the template searching inside initial contour are also concerned in this paper. Compared with some recent approaches, our method is more convincing and works well for synthetic textured images and ones in the real world.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Arsenault E, Baker C (2010) The role of higher-order statistics in naturalistic texture segmentation: modelling psychophysical data. J Vis 10:1354

    Article  Google Scholar 

  2. Aujol JF, Aubert G, Blanc-Féraud L, Chambolle A (2003) Decomposing an image: application to textured images and SAR images. INRIA Res Rep 4704

  3. Awate SP, Whitaker RT (2005) Higher-order image statistics for unsupervised, information-theoretic, adaptive, image filtering. Proc IEEE Conf Comput Vis Pattern Recognit 2:44–51

    Google Scholar 

  4. Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522

    Article  Google Scholar 

  5. Brox T, Weickert J (2004) A TV flow based local scale measure for texture discrimination. Proc 8th European Conf Comput Vis 2:578–590

    Google Scholar 

  6. BSDS500 image database, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html

  7. Chambolle A (2004) An algorithm for total variation minimization and applications. JMIV 20:89–97

    Article  MathSciNet  Google Scholar 

  8. Clausi DA, Deng H (2005) Design-based texture feature fusion using gabor filters and co-occurrence probabilities. IEEE Trans Image Process 14(7):925–936

    Article  Google Scholar 

  9. Clausi DA, Jernigan M (1998) A fast method to determine co-occurrence texture features. IEEE Trans Geosci Remote Sens 36(1):298–300

    Article  Google Scholar 

  10. Cutler R, Davis L (2000) Robust real-time periodic motion detection, analysis, and applications. IEEE Trans Pattern Anal Mach Intell 22(8):781–796

    Article  Google Scholar 

  11. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Proc IEEE Conf Comput Vis Pattern Recog 2:886–893

    Google Scholar 

  12. De Grandi GD, Lee J-S, Schuler DL (2007) Target detection and texture segmentation in polarimetric SAR images using a wavelet frame: theoretical aspects. IEEE Trans Geosci Remote Sens 45(11):3437–3453

    Article  Google Scholar 

  13. Deselaers T, Ferrari V (2010) Global and efficient self-similarity for object classification and detection. Proc IEEE Conf Comput Vis Pattern Recog 1:1633–1640

    Google Scholar 

  14. Dunn D, Higgins W (1995) Optimal Gabor filters for texture segmentation. IEEE Trans Image Process 4(7):964–974

    Google Scholar 

  15. Eckman JP et al (1987) Recurrence plots of dynamical systems. Europhys Lett 4:973–977

    Article  Google Scholar 

  16. Foote JT, Cooper ML (2003) Media segmentation using self-similarity decomposition. SPIE Proceedings Vol 5021, Storage and Retrieval for Media Databases, 167–175

  17. Gao Y, Bouix S, Shenton M, Tannenbaum A (2013) Sparse texture active contour. IEEE Trans Image Process 22(10):3866–3878

    Article  MathSciNet  Google Scholar 

  18. Kovesi P (1999, Summer) Image features from phase congruency. Videre: J Comput Vis Res. MIT Press. Volume 1, Number 3 http://mitpress.mit.edu/e-journals/Videre/001/v13.html

  19. Lele S (1993) Euclidean distance matrix analysis (EDMA): estimation of mean form and mean form difference. Math Geol 25:573–602

    Article  MATH  MathSciNet  Google Scholar 

  20. Lifei Z (2002) Active contour technique based on minimum description length principle. Ph.D. dissertation, Insititute of Electronics, Chinese Academy of Sciences

  21. Liu X, Wang D (2006) Image and texture segmentation using local spectral histograms. IEEE Trans Image Process 15(10):3066–3077

    Google Scholar 

  22. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  23. Maizel JV Jr, Lenk RP (1981) Enhanced graphic matrix analysis of nucleic acid and protein sequences. Proc Natl Acad Sci U S A 78(12):7665–7669

    Article  MathSciNet  Google Scholar 

  24. Malik J, Perona P (1990) Preattentive texture discrimination with early vision mechanisms. J Opt Soc Am A 7(5):923–932

    Article  Google Scholar 

  25. Manjunath B, Derin H (1991) Unsupervised texture segmentation using Markov random fields models. IEEE Trans Pattern Anal Mach Intell 13(5):478–482

    Article  Google Scholar 

  26. Meyer Y (2001, Mar) Oscillating patterns in image processing and nonlinear evolution equations. Univ Lect Ser 22, Amer Math Soc

  27. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630

    Article  Google Scholar 

  28. Osher SJ, Solé A, Vese LA (2003) Image decomposition and restoration using total variation minimization and the H-1 norm. Multiscale Model Simul SIAM Interdiscip J 1(3):349–370

    Article  MATH  Google Scholar 

  29. Paragios N, Deriche R (2002) Geodesic active regions and level set methods for supervised texture segmentation. Int J Comput Vis 46(3):223–247

    Article  MATH  Google Scholar 

  30. Rousson M, Brox T, Deriche R (2003) Active unsupervised texture segmentation on diffusion based feature space. Proc IEEE Conf Comput Vis Pattern Recognit 2:669–704

    Google Scholar 

  31. Sandberg B, Chan T, Vese L (2002) A level-set and gabor-based active contour algorithm for segmenting textured images. Proc UCLA Dept Math CAM Rep Conf 1–2

  32. Shapiro B, Nussinov R, Lipkin L, Maizel J Jr (1987) An interactive dot matrix system for locating potentially significant features in nucleic acid molecules. J Bimol Struct Dyn 4(5):697–706

    Article  Google Scholar 

  33. Shechtman E, Irani M (2007) Matching local self-similarities across images and videos. Proc IEEE Conf Comput Vis Pattern Recog 1–8

  34. Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4(11):1549–1560

    Article  Google Scholar 

  35. Vese LA, Osher SJ (2003) Modeling textures with total variation minimization and oscillating patterns in image processing. J Sci Comput 19:553–572

    Article  MATH  MathSciNet  Google Scholar 

  36. Wilscy M, Sasi RK (2010) Wavelet based texture segmentation. Proc IEEE Int Conf Comput Intell Comput Res (ICCIC) 1–4

  37. Zhang J, Tan T, Ma L (2002) Invariant texture segmentation via circular gabor filters. Proc Int Conf Pattern Recognit 3:901–904

    Google Scholar 

Download references

Acknowledgments

This work was financially supported by The Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201304115).

Author information

Authors and Affiliations

  1. The Experimental Teaching Center of Electronics information and Control, School of Automation, Beijing Information Science and Technology University, No.12, Qing He Xiao Ying Dong Lu, HaiDian Dist., Beijing, China, 100192

    Hongbo Yang

  2. School of Computer, Beijing Information Science and Technology University, No.12, Qing He Xiao Ying Dong Lu, HaiDian Dist., Beijing, China, 100192

    Xia Hou

Authors
  1. Hongbo Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xia Hou
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Hongbo Yang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, H., Hou, X. Texture segmentation using image decomposition and local self-similarity of different features. Multimed Tools Appl 74, 6069–6089 (2015). https://doi.org/10.1007/s11042-014-1909-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1909-2

Keywords