Machine Vision and Applications

, Volume 29, Issue 4, pp 677–687 | Cite as

An experimental comparison of superpixels detection methods for contour detection

  • Xuan-Yin Wang
  • Chang-Wei Wu
  • Ke Xiang
  • Sen-Wei Xiang
  • Wen Chen
Original Paper


Recently, many superpixels detection methods have been proposed and used in various applications. We are interested in which method is more suitable for the application of contour detection. In this paper, superpixels are evaluated on BSDS500 dataset in two different aspects. On the one hand, contours are directly provided by the boundaries of superpixels and experiments show that better results could be achieved by the superpixels with irregular shapes than those with regular shapes and similar sizes. On the other hand, contours are further detected from those candidate positions which are confirmed by the boundaries of superpixels through the operation of dilation. In this situation, experiments show that competitive results could also be achieved by some superpixels with regular shapes and similar sizes. Besides, we propose a superpixels detection method called watershed-based graph (WG), by which superpixels with irregular shapes could be produced. Firstly, a graph is constructed from an over-segmented map which is achieved through a watershed algorithm. Then, to get the desired superpixels, the graph is segmented by merging neighbor segments in an order of decreasing similarity. Experiments show that higher efficiency could be achieved by WG with a moderate worse contour quality than its original graph-based method.


Superpixels Region merging Contour detection Image segmentation 



This work is supported by the Science Fund for Creative Research Groups of National Natural Science Foundation of China (Grant No. 51521064) and by the innovation fund of Shanghai Academy of Spaceflight Technology (Grant No. SAST2015086).


  1. 1.
    Alex, L., Adrian, S., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Kaleem, S.: Turbopixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)CrossRefGoogle Scholar
  2. 2.
    Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)CrossRefGoogle Scholar
  3. 3.
    Tighe, J., Lazebnik, S.: Superparsing: scalable nonparametric image parsing with superpixels. Int. J. Comput. Vis. 101(2), 352–365 (2010)MathSciNetGoogle Scholar
  4. 4.
    Ren, Z., Shakhnarovich, G.: Image segmentation by cascaded region agglomeration. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2011–2018 (2013)Google Scholar
  5. 5.
    Shen, J., Du, Y., Li, X.: Interactive segmentation using constrained laplacian optimization. IEEE Trans. Circuits Syst. Video Technol. 24(7), 1088–1100 (2014)CrossRefGoogle 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 (2016). MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, W., Shen, J., Li, X., Porikli, F.: Robust video object cosegmentation. IEEE Trans. Image Process. 24(10), 3137–3148 (2015). MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dong, X., Shen, J., Shao, L.: Hierarchical superpixel-to-pixel dense matching. IEEE Trans. Circuits Syst. Video Technol. 27(12), 2518–2526 (2017). CrossRefGoogle Scholar
  9. 9.
    Wang, W., Shen, J., Shao, L.: Consistent video saliency using local gradient flow optimization and global refinement. IEEE Trans. Image Process. 24(11), 4185–4196 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wu, C.W., Zhao, H.Q., Cao, S.X., Xiang, K., Wang, X.Y.: Attention shift-based multiple saliency object segmentation. J. Electron. Imaging 25(5), 053,009–053,009 (2016). CrossRefGoogle Scholar
  11. 11.
    Han, J., Cheng, G., Li, Z., Zhang, D.: A unified metric learning-based framework for co-saliency detection. IEEE Trans. Circuits Syst. Video Technol. PP(99), 1 (2017). CrossRefGoogle Scholar
  12. 12.
    Zhang, D., Meng, D., Han, J.: Co-saliency detection via a self-paced multiple-instance learning framework. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 865–878 (2017). CrossRefGoogle Scholar
  13. 13.
    Yao, X., Han, J., Zhang, D., Nie, F.: Revisiting co-saliency detection: a novel approach based on two-stage multi-view spectral rotation co-clustering. IEEE Trans. Image Process. 26(7), 3196–3209 (2017). MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang, D., Han, J., Jiang, L., Ye, S., Chang, X.: Revealing event saliency in unconstrained video collection. IEEE Trans. Image Process. 26(4), 1746–1758 (2017). MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: Computer Vision and Pattern Recognition (2015)Google Scholar
  16. 16.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  17. 17.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  18. 18.
    Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)CrossRefGoogle Scholar
  19. 19.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  20. 20.
    Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices, pp. 1–8 (2008)Google Scholar
  21. 21.
    Moore, A.P., Prince, S.J.D., Warrell, J.: Lattice cut—constructing superpixels using layer constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2124 (2010)Google Scholar
  22. 22.
    Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Computer Vision—ECCV 2010—European Conference on Computer Vision, Heraklion, Crete, Greece, September 5–11, 2010, Proceedings, pp. 211–224 (2010)Google Scholar
  23. 23.
    Zhang, Y., Hartley, R., Mashford, J., Burn, S.: Superpixels via pseudo-boolean optimization. In: International Conference on Computer Vision, pp. 1387–1394 (2011)Google Scholar
  24. 24.
    Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2104 (2011)Google Scholar
  25. 25.
    Bergh, M.V.D., Boix, X., Roig, G., Gool, L.V.: Seeds: Superpixels extracted via energy-driven sampling. Int. J. Comput. Vis. 111(3), 298–314 (2012)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  27. 27.
    Neubert, P., Protzel, P.: Compact watershed and preemptive slic: On improving trade-offs of superpixel segmentation algorithms. In: International Conference on Pattern Recognition, pp. 996–1001 (2014)Google Scholar
  28. 28.
    Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014). MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., Shao, L.: Real-time superpixel segmentation by dbscan clustering algorithm. IEEE Trans. Image Process. 25(12), 5933–5942 (2016). MathSciNetCrossRefGoogle Scholar
  30. 30.
    Peng, J., Shen, J., Yao, A., Li, X.: Superpixel optimization using higher order energy. IEEE Trans. Circuits Syst. Video Technol. 26(5), 917–927 (2016). CrossRefGoogle Scholar
  31. 31.
    Liang, Y., Shen, J., Dong, X., Sun, H., Li, X.: Video supervoxels using partially absorbing random walks. IEEE Trans. Circuits Syst. Video Technol. 26(5), 928–938 (2016). CrossRefGoogle Scholar
  32. 32.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  33. 33.
    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
  34. 34.
    Deng, Y., Manjunath, B.S., Shin, H.: Color image segmentation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2. IEEE (1999)Google Scholar
  35. 35.
    Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundam. Inf. 41(1–2), 187–228 (2000)MathSciNetzbMATHGoogle Scholar
  36. 36.
    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
  37. 37.
    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: Eighth IEEE International Conference on Computer Vision, 2001. ICCV 2001. Proceedings, vol. 2, pp. 416–423 (2001)Google Scholar
  38. 38.
    Wang, X.Y., Wu, C.W., Xiang, K., Chen, W.: Efficient local and global contour detection based on superpixels. J. Vis. Commun. Image Represent. 48, 77–87 (2017). CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Fluid Power and Mechatronic Systems, Mechanical EngineeringZhejiang UniversityHangzhouChina
  2. 2.Shanghai Key Laboratory of Aerospace Intelligent Control TechnologyShanghai Institute of Spaceflight Control TechnologyShanghaiChina

Personalised recommendations