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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Tsinghua University Press (1991)
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)
Grady, L.: Random Walks for Image Segmentation. IEEE Computer Society (2006)
Jia, Z.: A learning-based framework for depth ordering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 294–301 (2012)
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_20
Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: Computer Vision and Pattern Recognition, pp. 1356–1363 (2015)
Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: Computer Vision and Pattern Recognition, pp. 2097–2104 (2011)
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)
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)
Neubert, P.: Superpixels and their application for visual place recognition in changing environments (2015)
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)
Ren, X.: Learning a classification models for segmentation. In: Proceedings of the IEEE International Conference Computer Vision (2003)
Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)
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)
Trefethen, L.N., Bau, D.: Numerical Linear Algebra. Society for Industrial and Applied Mathematics (1997)
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)
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)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, L., Kang, X., Ming, A., Zhang, X. (2019). Dynamic Random Walk for Superpixel Segmentation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-20876-9_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20875-2
Online ISBN: 978-3-030-20876-9
eBook Packages: Computer ScienceComputer Science (R0)