Skip to main content
SpringerLink
Log in

Diffusion map based interactive image segmentation

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

Abstract

Image segmentation is a necessary but difficult task in many image processing applications. Unlike conventional auto-segmentation, semi-supervised image segmentation involves a moderate amount of user interaction. In this paper, a novel interactive image segmentation method based on diffusion maps is proposed, which can better account for the distribution of pixels in feature space. Our method replaces the pixel features of Euclidean distance with diffusion maps and performs feature optimization and dimensionality reduction in order to achieve a final feature vector, with the assistance of user interaction. On this basis, L0 gradient minimization is applied to achieve steady segmentation results. Finally, the K-means clustering algorithm is applied to perfect the segmentation. Experiments show that our method can obtain better segmentation results than several state-of-the-art interactive image segmentation methods.

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

Similar content being viewed by others

References

  1. Belkin M, Niyogi P, Sindhwani V (2005) On manifold regularization. In: Proceedings of the artificial intelligence and statistics (AISTATS). Savannah, pp 17–24

  2. Belkin M, Niyogi P, Sindhwami V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7 (1):2399–2434

    MathSciNet  MATH  Google Scholar 

  3. Boykov Y, Funka-Lea G (2006) Graph cuts and efficient n-d image segmentation. Int J Comput. Vis 70:109–131

    Article  Google Scholar 

  4. Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. MIT Press, Cambridge. http://www.kyb.tuebingen.mpg.de/ssl-book

    Book  Google Scholar 

  5. Coifman RR, Lafon S (2006) Diffusion maps. Appl Comput Harmon Anal 21 (1):5–30. special Issue: Diffusion Maps and Wavelets

    Article  MathSciNet  MATH  Google Scholar 

  6. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  7. Fischer B, Buhmann J (2003) Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Trans Pattern Anal Mach Intell 25(4):513–518

    Article  Google Scholar 

  8. Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783

    Article  Google Scholar 

  9. Grady L, Sinop A (2008) Fast approximate random walker segmentation using eigenvector precomputation. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008, pp 1–8

  10. Houhou N, Bresson X, Szlam A, Chan TF (2009) Semi-supervised segmentation based on non-local continuous min-cut. In: The international conference on scale space and variational methods in computer vision

  11. Leung T, Malik J (1998) Contour continuity in region based image segmentation. In: 5th European conference on computer vision

  12. Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242

    Article  Google Scholar 

  13. Li Y, Sun J, Tang C-K, Shum H-Y (2004) Lazy snapping. ACM Trans Graph 23(3):303–308

    Article  Google Scholar 

  14. Meila M, Shi J (2001) Learning segmentation by random walks. In: Advances in neural information processing systems. MIT Press, pp 873–879

  15. Meila M, Shi J (2001) A random walks view of spectral segmentation. In: AISTATS

  16. Nadler B, Lafon S, Coifman RR, Kevrekidis IG (2005) Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators. In: Advances in neural information processing systems, vol 18. MIT Press, pp 955–962

  17. Nguyen TNA, Cai J, Zhang J, Zheng J (2012) Robust interactive image segmentation using convex active contours. IEEE Trans Image Process 21(8):3734–3743

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  19. Wang X, Davidson I (2010) Flexible constrained spectral clustering. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’10. ACM, New York, pp 563–572

  20. Wang F, Zhang C, Shen H, Wang J (2006) Semi-supervised classification using linear neighborhood propagation. In: IEEE computer society conference on computer vision and pattern recognition, 2006, vol 1, pp 160–167

  21. Weiss Y (1999) Segmentation using eigenvectors: a unifying view. In: The proceedings of the 7th IEEE international conference on computer vision, 1999, vol 2, pp 975–982

  22. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via l0 gradient minimization. ACM Trans Graph 30(6):174:1–174:12

    Google Scholar 

  23. Yu Z, Luo P, You J, Wong H-S, Leung H, Wu S, Zhang J, Han G (2016) Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Trans Knowl Data Eng 28(3):701–714

    Article  Google Scholar 

  24. Zhang C, Qin Y, Zhu X, Zhang J, Zhang S (2006) Clustering-based missing value imputation for data preprocessing. In: IEEE international conference on industrial informatics, pp 1081–1086

  25. Zhu X, Li X, Zhang S, Ju C, Wu X (2016) Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans Neural Netw Learn Syst 1–13

Download references

Acknowledgments

This work is supported in part by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2014BAK14B01), National Natural Science Foundation of China (No. 61379075, 61472363), Natural Science Foundation of Zhejiang Province (No. LY15F020006, LR12F02001).

Author information

Authors and Affiliations

  1. Zhejiang Gongshang University, No. 18, Xuezheng Street, Xiasha University Town, Hangzhou, China

    Xun Wang, Jianqiu Jin & Bailin Yang

Authors
  1. Xun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianqiu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bailin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianqiu Jin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Jin, J. & Yang, B. Diffusion map based interactive image segmentation. Multimed Tools Appl 76, 17497–17509 (2017). https://doi.org/10.1007/s11042-016-4106-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4106-7

Keywords

Search

Navigation