Multimedia Tools and Applications

, Volume 76, Issue 24, pp 25713–25729 | Cite as

Spectral segmentation via minimum barrier distance

  • Jing Mao Zhang
  • Yan Xia ShenEmail author


Constructing a reliable affinity matrix is crucial for spectral segmentation. In this paper, we define a technique to create a reliable affinity matrix for the application to spectral segmentation. We propose an affinity model based on the minimum barrier distance (MBD). First, the image is over-segmented into superpixels; then the subset of the pixels, located in the center of these superpixels, is used to compute the MBD-based affinities of the original image, with particular care taken to avoid a strong boundary, as described in the classical model. To deal with images with faint object and random or “clutter” background, we present gradient data that are integrated with the MBD data. To capture different perceptual grouping cues, the completed affinity model includes MBD, color, and spatial cues of the image. Finally, spectral segmentation is implemented at the superpixel level to provide an image segmentation result with pixel granularity. Experiments using the Berkeley image segmentation database validate the effectiveness of the proposed method. Covering, PRI, VOI, and the F-measure are used to evaluate the results relative to several state-of-the-art algorithms.


Spectral segmentation Minimum barrier distance Affinity model Image segmentation 



The work was supported by National Nature Science Foundation (Grant No. 61573167, 61572237).the Fundamental Research Funds for the Central Universities (Grant No. JUSRP31106, JUSRP51510).


  1. 1.
    Achanta R, Shaji A, Smith K et al (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis & Machine Intelligence 34(11):2274–2282CrossRefGoogle Scholar
  2. 2.
    Arbelaez P. (2006) Boundary extraction in natural images using ultrametric contour maps. 2006 Conf Comput Vis Pattern Recognit Workshop (CVPRW'06), pp 182-182Google Scholar
  3. 3.
    Arbeláez P, Maire M, Fowlkes C et al (2011) Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence 33(5):898–916CrossRefGoogle Scholar
  4. 4.
    Aytekin C, Kiranyaz S, Gabbouj M (2014) Automatic object segmentation by quantum cuts. ICPR, pp 112–117. doi: 10.1109/ICPR.2014.29
  5. 5.
    Aytekin Ç, Ozan EC, Kiranyaz S, et al (2016) Extended quantum cuts for unsupervised salient object extraction. Multimed Tools Appl pp 1-21. doi: 10.1007/s11042-016-3431-1
  6. 6.
    Belongie S, Carson C, Greenspan H, et al. (1998) Color- and texture-based image segmentation using EM and its application to content-based image retrieval. IEEE Int Conf Comput Vis. pp 675-682Google Scholar
  7. 7.
    Chen L, Zou J, Chen CP (2014) Kernel spatial shadowed c-means for image segmentation. International Journal of Fuzzy Systems 16(1):46–56MathSciNetGoogle Scholar
  8. 8.
    Chung CH, Cheng SC, Chang CC (2010) Adaptive image segmentation for region-based object retrieval using generalized Hough transform. Pattern Recogn 43(10):3219–3232CrossRefzbMATHGoogle Scholar
  9. 9.
    Ciesielski KC, Strand R, Malmberg F et al (2014) Efficient algorithm for finding the exact minimum barrier distance ☆. Computer Vision & Image Understanding 123(2):53–64CrossRefGoogle Scholar
  10. 10.
    Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence 24(5):603–619CrossRefGoogle Scholar
  11. 11.
    Cour T, Benezit F, Shi J. (2005) Spectral segmentation with multiscale graph decomposition. 2005 I.E. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 2: 1124–1131Google Scholar
  12. 12.
    Dollar P, Zitnick CL (2015) Fast edge detection using structured forests. IEEE Transactions on Pattern Analysis & Machine Intelligence 37(8):1558–1570CrossRefGoogle Scholar
  13. 13.
    Dougherty ER, Lotufo RA (2003) Hands-on morphological image processing. SPIE Press, BellinghamGoogle Scholar
  14. 14.
    Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation [J]. Int J Comput Vis 59(59):167–181CrossRefGoogle Scholar
  15. 15.
    Karadag OO, Vural FTY (2014) Image segmentation by fusion of low level and domain specific information via Markov random fields. Pattern Recogn Lett 46:75–82CrossRefGoogle Scholar
  16. 16.
    Kim TH, Lee KM, Lee SU (2013) Learning full pairwise affinities for spectral segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence 35(7):1690–1703CrossRefGoogle Scholar
  17. 17.
    Kim S, Nowozin S, Kohli P et al (2015) Higher-order correlation clustering for image segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence 36(36):1761–1774Google Scholar
  18. 18.
    Kumar MP, Koller D. (2010) Efficiently selecting regions for scene understanding. Comput Vis Pattern Recognit (CVPR), IEEE conference on. IEEE, pp 3217–3224Google Scholar
  19. 19.
    Lee YJ, Grauman K (2010) Object-graphs for context-aware category discovery. IEEE Comput Vis Pattern Recognit, pp 1–8. doi: 10.1109/CVPR.2010.5540237
  20. 20.
    Li Z, Wu XM, Chang SF (2012) Segmentation using superpixels: A bipartite graph partitioning approach. 2012 I.E. Conf Comput Vis Pattern Recognit (CVPR), pp 789–796Google Scholar
  21. 21.
    Li X, Jin L, Song E et al (2016) An integrated similarity metric for graph-based color image segmentation. Multimedia Tools and Applications 75(6):2969–2987CrossRefGoogle Scholar
  22. 22.
    Liu Q, Han T, Sun Y et al (2013) A two step salient objects extraction framework based on image segmentation and saliency detection. Multimedia Tools & Applications 67(1):231–247CrossRefGoogle Scholar
  23. 23.
    Lu H, Zhang R, Li S et al (2013) Spectral segmentation via midlevel cues integrating geodesic and intensity. Cybernetics IEEE Transactions on 43(6):2170–2178CrossRefGoogle Scholar
  24. 24.
    Malik J, Shi J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  25. 25.
    Meilǎ M (2005) Comparing clusterings: an axiomatic view. Proceedings of the 22nd international conference on machine learning. ACM, pp 577‑584Google Scholar
  26. 26.
    Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Adv Neural Inf Proces Syst 2:849–856Google Scholar
  27. 27.
    Ponttuset J, Arbelaez P, Barron J, et al (2015) Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Trans Pattern Anal Mach Intell. pp 1–1Google Scholar
  28. 28.
    Price BL, Morse B, Cohen S. (2010) Geodesic graph cut for interactive image segmentation. 2010 I.E. Conf Comput Vis Pattern Recognit (CVPR), pp 3161–3168Google Scholar
  29. 29.
    Rohkohl C, Engel K. (2007) Efficient image segmentation using pairwise pixel similarities. Dagm Conf Pattern Recognit. Springer-Veerlag. 254-263Google Scholar
  30. 30.
    Strand R, Ciesielski KC, Malmberg F et al (2013) The minimum barrier distance. Computer Vision & Image Understanding 117(4):429–437CrossRefGoogle Scholar
  31. 31.
    Sumengen B, Bertelli L, Manjunath BS (2006) Fast and adaptive pairwise similarities for graph cuts-based image segmentation. 2006 CVPRW '06. IEEE Conf Comput Vis Pattern Recognit Workshop, pp 179–179Google Scholar
  32. 32.
    Tang C, Hou C, Wang P et al (2015) Salient object detection using color spatial distribution and minimum spanning tree weight. Multimedia Tools & Applications 75(12):1–16Google Scholar
  33. 33.
    Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis & Machine Intelligence 29(6):929–944CrossRefGoogle Scholar
  34. 34.
    Wang X, Tang Y, Masnou S et al (2015) A global/local affinity graph for image segmentation. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 24(4):1399–1411MathSciNetCrossRefGoogle Scholar
  35. 35.
    William M (1971) Rand. Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850CrossRefGoogle Scholar
  36. 36.
    Yang K, Hua XS, Wang M et al (2011) Tag tagging: towards more descriptive keywords of image content. IEEE Transactions on Multimedia 13(4):662–673CrossRefGoogle Scholar
  37. 37.
    Yu Z, Li A, Au OC, et al. (2012) Bag of textons for image segmentation via soft clustering and convex shift. 2012 I.E. Conf Comput Vis Pattern Recognit (CVPR), pp 781–788Google Scholar
  38. 38.
    Zhou D, Bousquet O, Lal TN et al (2004) Learning with local and global consistency. Adv Neural Inf Proces Syst 16(4):321–328Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.the Engineering Research Center of IoT Technology and Application of the Ministry of EducationJiangnan UniversityWuXiChina

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