Advertisement

Dynamic Random Walk for Superpixel Segmentation

  • Lei Zhu
  • Xuejing KangEmail author
  • Anlong Ming
  • Xuesong Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11366)

Abstract

In this paper, we present a novel Random Walk model called Dynamic Random Walk (DRW) for superpixel segmentation. The proposed DRW adds a new type of node called dynamic node to enrich the features of labels and reduce redundant calculation. By greedily optimizing the Weighted Random Walk Entropy (WRWE), our DRW can consider the features of both seed nodes and dynamic nodes, which enhances the boundary adherence. In addition, a new seed initialization strategy, which can evenly distribute seed nodes in both 2D and 3D space, is proposed to extend our DRW for superpixel segmentation. With this strategy, our DRW can generate superpixels in only one iteration without updating seed nodes. The experiment results show that our DRW is faster than existing RW models, and better than the state-of-the-art superpixel segmentation algorithms in both efficiency and the performance.

Notes

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (61701036, 61871055), Fundamental Research Funds for the Central Universities (2018RC54).

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Achanta, R., Susstrunk, S.: Superpixels and polygons using simple non-iterative clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4895–4904 (2017)Google Scholar
  3. 3.
    Amer, M.R., Yousefi, S., Raich, R., Todorovic, S.: Monocular extraction of 2.1D sketch using constrained convex optimization. Int. J. Comput. Vis. 112(1), 23–42 (2015)CrossRefGoogle Scholar
  4. 4.
    Bergh, M.V.D., Boix, X., Roig, G., Capitani, B.D., Gool, L.V.: Seeds: superpixels extracted via energy-driven sampling. Int. J. Comput. Vis. 111(3), 298–314 (2013)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Tsinghua University Press (1991)Google Scholar
  6. 6.
    Dong, X., Shen, J., Shao, L., Gool, L.V.: Sub-Markov random walk for image segmentation. IEEE Trans. Image Process. 25(2), 516–527 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Grady, L.: Random Walks for Image Segmentation. IEEE Computer Society (2006)Google Scholar
  8. 8.
    Jia, Z.: A learning-based framework for depth ordering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 294–301 (2012)Google Scholar
  9. 9.
    Kim, T.H., Lee, K.M., Lee, S.U.: Generative image segmentation using random walks with restart. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 264–275. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88690-7_20CrossRefGoogle Scholar
  10. 10.
    Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: Computer Vision and Pattern Recognition, pp. 1356–1363 (2015)Google Scholar
  11. 11.
    Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: Computer Vision and Pattern Recognition, pp. 2097–2104 (2011)Google Scholar
  12. 12.
    Liu, Y.J., Yu, C.C., Yu, M.J., He, Y.: Manifold SLIC: a fast method to compute content-sensitive superpixels. In: Computer Vision and Pattern Recognition, pp. 651–659 (2016)Google Scholar
  13. 13.
    Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)CrossRefGoogle Scholar
  14. 14.
    Neubert, P.: Superpixels and their application for visual place recognition in changing environments (2015)Google Scholar
  15. 15.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)CrossRefGoogle Scholar
  16. 16.
    Ren, X.: Learning a classification models for segmentation. In: Proceedings of the IEEE International Conference Computer Vision (2003)Google Scholar
  17. 17.
    Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., Shao, L.: Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans. Image Process. Publ. IEEE Sig. Process. Soc. 25(12), 5933–5942 (2016)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Trefethen, L.N., Bau, D.: Numerical Linear Algebra. Society for Industrial and Applied Mathematics (1997)Google Scholar
  20. 20.
    Wu, X.M., Li, Z., So, M.C., Wright, J., Chang, S.F.: Learning with partially absorbing random walks. In: Advances in Neural Information Processing Systems, pp. 3086–3094 (2012)Google Scholar
  21. 21.
    Yan, J., Yu, Y., Zhu, X., Lei, Z., Li, S.Z.: Object detection by labeling superpixels. In: Computer Vision and Pattern Recognition, pp. 5107–5116 (2015)Google Scholar
  22. 22.
    Yeo, D., Son, J., Han, B., Han, J.H.: Superpixel-based tracking-by-segmentation using Markov chains. In: Computer Vision and Pattern Recognition, pp. 511–520 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lei Zhu
    • 1
  • Xuejing Kang
    • 1
    Email author
  • Anlong Ming
    • 1
  • Xuesong Zhang
    • 1
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations