Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Topology-based image segmentation using LBP pyramids

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper, we present a new image segmentation algorithm which is based on local binary patterns (LBPs) and the combinatorial pyramid and which preserves structural correctness and image topology. For this purpose, we define a codification of LBPs using graph pyramids. Since the LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region, we use it to obtain a “minimal” image representation in terms of the topological characterization of a given 2D grayscale image. Based on this idea, we further describe our hierarchical texture aware image segmentation algorithm and compare its segmentation output and the “minimal” image representation.

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

Access this article

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.

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

Similar content being viewed by others

Notes

  1. More images and segmentation results can be found at: http://prip.tuwien.ac.at/research/scis_results.zip.

References

  1. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE TPAMI 33(5), 898–916 (2011)

    Article  Google Scholar 

  2. Cerman, M.: Structurally correct image segmentation using local binary patterns and the combinatorial pyramid. Technical Report 133(ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr133), Vienna University of Technology, Pattern Recognition and Image Processing (PRIP) Group (2015)

  3. Cerman, M., Gonzalez-Diaz, R., Kroptasch, W.: LBP and irregular graph pyramids. In: 16th International Conference on Computer Analysis of Images and Patterns (CAIP2015) (2015)

  4. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE TPAMI 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  5. Fehr, J., Burkhardt, H.: 3D rotation invariant local binary patterns. In: Proceedings of 19th International Conference on Pattern Recognition (ICPR08), pp. 1–4 (2008)

  6. Felzenszwalb, P.F., Huttenlocher, D.P.: Image segmentation using local variation. In: Proceedings of Computer Vision and Pattern Recognition, pp. 98–104 (1998)

  7. Gonzalez-Diaz, R., Ion, A., Iglesias-Ham, M., Kropatsch, W.G.: Invariant representative cocycles of cohomology generators using irregular graph pyramids. Comput. Vis. Image Underst. 115(7), 1011–1022 (2011)

    Article  MATH  Google Scholar 

  8. Guo, G., Jones, M.J.: Iris extraction based on intensity gradient and texture difference. In: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (WACV ’08), pp. 1–6 (2008)

  9. Hadid, A., Pietikinen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04), pp. 797–804 (2004)

  10. Haxhimusa, Y.: The Structurally Optimal Dual Graph Pyramid and its Application in Image Partitioning, 1st edn. IOS Press, Amsterdam (2007)

    MATH  Google Scholar 

  11. Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE TPAMI 28(4), 657–662 (2006)

    Article  Google Scholar 

  12. Kropatsch, W.G.: Building irregular pyramids by dual-graph contraction. IEEE Proc. Vis. Image Signal Process. 142(6), 366–374 (1995)

    Article  Google Scholar 

  13. Kropatsch, W.G., Haxhimusa, Y., Pizlo, Z., Langs, G.: Vision pyramids that do not grow too high. Pattern Recognit. Lett. 26(3), 319–337 (2005)

    Article  Google Scholar 

  14. Latecki, L., Eckhardt, U., Rosenfeld, A.: Well-composed sets. Comput. Vis. Image Underst. 61, 70–83 (1995)

    Article  Google Scholar 

  15. LBP’2014 Workshop on Computer Vision With Local Binary Pattern Variants https://sites.google.com/site/lbp2014ws/

  16. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 8th IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 416–423 (2001)

  17. Moyles, D.M., Thompson, G.L.: An algorithm for finding a minimum equivalent graph of a digraph. J. ACM 16(3), 455–460 (1969)

    Article  MATH  Google Scholar 

  18. Nguyen, D.T., Ogunbona, P.O., Li, W.: A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recognit. 46(5), 1485–1500 (2013)

    Article  Google Scholar 

  19. Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)

    Article  Google Scholar 

  20. Ojala, T., Pietikinen, M.: Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32, 477–486 (1999)

    Article  Google Scholar 

  21. Ojala, T., Pietikainen, M., Maenaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE TPAMI 24(7), 971–987 (2002)

    Article  Google Scholar 

  22. Paris, S., Durand, F.: A topological approach to hierarchical segmentation using mean shift. CVPR 2007, IEEE Computer Society 2007, ISBN 1-4244-1179-3

  23. Paulhac, L., Makris, P., Ramel, J.-Y.: Comparison between 2D and 3D local binary pattern methods for characterization of three-dimensional textures. In: Proceedings of the International Conference on Image Analysis and Recognition, LNCS, vol. 5112, pp. 670–679 (2008)

  24. Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recognit. 46(3), 1020–1038 (2013)

    Article  Google Scholar 

  25. Pietikinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, vol. 40. Springer, NewYork (2011)

    Book  Google Scholar 

  26. Raut, S., Raghuvanshi, M., Dharaskar, R., Raut, A.: Image segmentation—a state-of-art survey for prediction. In: Proceedings of the 2009 International Conference on Advanced Computer Control (ICACC), pp. 420–424 (2009)

  27. Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognit. Lett. 33(4), 431–437 (2012)

    Article  Google Scholar 

  28. Sharma, G., Wu, W., Dalal, E.N.: The CIEDE2000 color difference formula: implementation notes, supplementary test data, and mathematical observations. Color Res. Appl. 30(1), 21–30 (2005)

    Article  Google Scholar 

Download references

Acknowledgments

We thank both referees for their valuable comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ines Janusch.

Additional information

Author was partially supported by IMUS and Spanish Ministry under grant MTM2015-67072-P (MINECO/FEDER, UE).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cerman, M., Janusch, I., Gonzalez-Diaz, R. et al. Topology-based image segmentation using LBP pyramids. Machine Vision and Applications 27, 1161–1174 (2016). https://doi.org/10.1007/s00138-016-0795-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-016-0795-1

Keywords

Navigation