Skip to main content
SpringerLink
Log in

High precision image segmentation algorithm using SLIC and neighborhood rough set

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

Abstract

A high precision image segmentation algorithm using SLIC and neighborhood rough set is proposed. The algorithm mainly includes two stages: the stage of superpixel generation and the mergence stage based on neighborhood rough set. In superpixel generation stage, based on L-channel color histogram and its peak, the scheme of initial superpixel number generation is proposed according to the complexity of the image itself. For inaccuracy segmentation edge of SLIC caused by isolated pixels, the compactness factor is appropriately increased before they are generated. After that, the scheme of reclassifying each isolated pixel is proposed just relying on the color space. In superpixel mergence stage based on neighborhood rough set, the texture information using the gray level co-occurrence matrix is introduced into the feature representation of superpixel. It can reduce the dependence of color feature and improve the accuracy of the mergence. By constructing the information table, the neighborhood granule of each superpixel is acquired under the neighborhood threshold. Finally, the superpixels within the neighborhood granule are merged on the basis of the spatial adjacency between superpixels. In Berkeley segmentation data set, compared with the SLIC algorithm, the schemes of initial superpixel number generation and the isolated pixels processing are proved to be effective. Furthermore, the experiments demonstrate that the proposed algorithm can produce high-quality and high-precision image segmentation results in comparison with the SLIC-based image segmentation algorithms on three standard metrics.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Achanta R, Süsstrunk S (2017) Superpixels and polygons using simple non-iterative clustering. Proc IEEE Conf Comput Vis Pattern Recognit: 4895–4904

  2. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  3. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  4. Ates H F, Sunetci S, Ak K E (2016) Kernel likelihood estimation for superpixel image parsing. Proc Int Conf Image Anal Recogn: 234–242

  5. Bai XD, Cao ZG, Wang Y, Ye MN, Zhu L (2014) Image segmentation using modified SLIC and Nystrom based spectral clustering. Optik-Int J Light Elect Opt 125(16):4302–4307

    Article  Google Scholar 

  6. Bergh M V, Boix X, Roig G, Capitani B D (2012) Seeds: Superpixels extracted via energy-driven sampling. Proc IEEE Conf Eur Conf Comput Vision: 13–26

  7. Chen Y, Zeng Z, Lu J (2016) Neighborhood rough set reduction with fish swarm algorithm. Soft Comput 21(23):6907–6918

    Article  Google Scholar 

  8. Chen H, Li T, Cai Y et al (2016) Parallel attribute reduction in dominance-based neighborhood rough set. Inf Sci 373:351–368

    Article  Google Scholar 

  9. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181

    Article  Google Scholar 

  10. Hsu C Y, Ding J J (2013) Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging. Proceedings of the IEEE 9th international conference on information, communications and Signal Process: 1–5

  11. Hu Q, Yu D, Liu J, Wu C (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594

    Article  MathSciNet  Google Scholar 

  12. Huang G, Pun CM (2015) Robust interactive segmentation using color histogram and contourlet transform. Int J Comput Theory Eng 7(6):489

    Article  Google Scholar 

  13. Li Z, Chen J (2015) Superpixel segmentation using linear spectral clustering. Proc IEEE Conf Comput Vis Pattern Recognit: 1356–1363

  14. Li B, Xiao J, Wang X et al (2018) Feature reduction for power system transient stability assessment based on neighborhood rough set and discernibility matrix. Energies 11(1):185

    Article  Google Scholar 

  15. Liu M Y, Tuzel O, Ramalingam S, Chellappa R (2011) Entropy rate superpixel segmentation. Proc IEEE Conf Comput Vis Pattern Recognit: 2097–2104

  16. Liu B, Hu H, Wang H et al (2013) Superpixel-based classification with an adaptive number of classes for polarimetric SAR images. IEEE Trans Geosci Remote Sens 51(2):907–924

    Article  Google Scholar 

  17. Liu F, Lin G, Shen C (2015) CRF learning with CNN features for image segmentation. Pattern Recogn 48(10):2983–2992

    Article  Google Scholar 

  18. Liu Y, Yang J, Chen Y et al (2017) Stability analysis of hyperspectral band selection algorithms based on neighborhood rough set theory for classification. ChemometIntell Lab Syst 169:35–44

    Article  Google Scholar 

  19. Lucchi A, Li Y, Smith K, Fua P (2012) Structured image segmentation using kernelized features. Proc IEEE Conf Eur Conf Comput Vision: 400–413

  20. Peng B, Zhang L, Zhang D (2011) Automatic image segmentation by dynamic region merging. IEEE Trans Image Process 20(12):3592–3605

    Article  MathSciNet  Google Scholar 

  21. Rantalankila P, Kannala J, Rahtu E (2014) Generating object segmentation proposals using global and local search. Proc IEEE Conf Comput Vis Pattern Recognit:2417–2424

  22. Ren X, Malik J (2003) Learning a classification model for segmentation. Proc IEEE Int Conf Comput Vision: 10–17

  23. Shen J, Hao X, Liang Z et al (2016) Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans Image Process 25(12):5933–5942

    Article  MathSciNet  Google Scholar 

  24. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  25. Singla A, Patra S (2017) A fast automatic optimal threshold selection technique for image segmentation. SIViP 11(2):243–250

    Article  Google Scholar 

  26. Tighe J, Lazebnik S (2010) Superparsing: scalable nonparametric image parsing with superpixels. Proc IEEE Conf Eur Conf Comput Vision: 352–365

  27. Wang S, Lu H, Yang F, Yang M H (2011) Superpixel tracking. Proc IEEE Conf Int Conf Comput Vision: 1323–1330

  28. Wang C, Shao M, He Q et al (2016) Feature subset selection based on fuzzy neighborhood rough sets. Knowl-Based Syst 111:173–179

    Article  Google Scholar 

  29. Wang M, Liu X, Gao Y et al (2017) Superpixel segmentation: a benchmark. Signal Process Image Commun 56:28–39

    Article  Google Scholar 

  30. Zhang L, Yang Y, Gao Y et al (2014) A probabilistic associative model for segmenting weakly supervised images. IEEE Trans Image Process 23(9):4150–4159

    Article  MathSciNet  Google Scholar 

  31. Zhang K, Zhang L, Lam KM, Zhang D (2016) A level set approach to image segmentation with intensity inhomogeneity. IEEE Trans Cybernet 46(2):546–557

    Article  Google Scholar 

  32. Zhang Y, Li X, Gao X et al (2017) A simple algorithm of superpixel segmentation with boundary constraint. IEEE transactions on circuits and systems for video. Technology 27(7):1502–1514

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (61503271) and the Shanxi Scholarship Council of China (2016–044).

Author information

Authors and Affiliations

  1. College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, 030024, People’s Republic of China

    Xinlin Xie, Gang Xie & Xinying Xu

  2. College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, People’s Republic of China

    Gang Xie

Authors
  1. Xinlin Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinying Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Xie.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, X., Xie, G. & Xu, X. High precision image segmentation algorithm using SLIC and neighborhood rough set. Multimed Tools Appl 77, 31525–31543 (2018). https://doi.org/10.1007/s11042-018-6150-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6150-y

Keywords

Search

Navigation