Multimedia Tools and Applications

, Volume 76, Issue 8, pp 11097–11110 | Cite as

Unsupervised segmentation evaluation: an edge-based method



Unsupervised segmentation evaluation method quantifies the quality of segmentation without the reference segmentation or user assistance. Although some methods have been proposed to statistically analyze the pixel values, these methods are not sensitive enough to provide a metric of segmentation quality. This paper uses the image edge, a more robust feature, to measure the quality of segmentation. An edge-based segmentation evaluation method is introduced in this paper, which can be applied to both image and single region segmentation evaluation. The proposed method evaluates the quality of segmentation with three edge-based measures: the edge fitness, the intra-region edge error, and the out-of-bound error. These measures encourage the outline of segmentation to align with the edge and punish the segmentation that exceeds the edge. Experiments results show that our method is more sensitive to under-segmentation and over-segmentation. Using the parameters optimized by the proposed method, the segmentation produced by the classic region growing method is visually similar to the state-of-the-art segmentation method.


Image segmentation Objective evaluation Unsupervised evaluation 



This work is supported by National Natural Science Foundation of China (61170193, 61370102, 61370185), Guangdong Natural Science Foundation (S2012010009865, S2012020011081, S2013010013432, S2013010015940, 2014A030306050), Science and Technology Planning Project of Guangdong Province (2011B090400041, 2012B010100039, 2012B040305011, 2012B010100040), Education and Science Programs of Guangdong Province (11JXZ012,14JXN065), Guangdong Higher Education Discipline and Profession Special Fund Projects(2013KJCX0174), Science and Technology Planning Project of Huizhou City (2011P002, 2011g012, 2011P005, 2011P003, 2011g011, 2013B020015008, 2014B020004026) and the Fundamental Research Funds for the Central Universities, SCUT (2015PT022).


  1. 1.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRefGoogle Scholar
  2. 2.
    Borsotti M, Campadelli P, Schettini R (1998) Quantitative evaluation of color image segmentation results. Pattern Recogn Lett 19(8):741–747CrossRefMATHGoogle Scholar
  3. 3.
    Carreira J, Sminchisescu C (2012) Cpmc: automatic object segmentation using constrained parametric min-cuts. IEEE Trans Pattern Anal Mach Intell 34(7):1312–1328CrossRefGoogle Scholar
  4. 4.
    Chabrier S, Emile B, Rosenberger C, Laurent H (2006) Unsupervised performance evaluation of image segmentation. EURASIP Journal on Applied Signal Processing 2006:217–217MathSciNetGoogle Scholar
  5. 5.
    Chen HC, Wang SJ (2004) The use of visible color difference in the quantitative evaluation of color image segmentation. In: IEEE international conference on acoustics, speech, and signal processing, 2004. Proceedings (ICASSP’04), vol 3. IEEE, pp iii–593Google Scholar
  6. 6.
    Corcoran P, Winstanley A, Mooney P (2010) Segmentation performance evaluation for object-based remotely sensed image analysis. Int J Remote Sens 31 (3):617–645CrossRefGoogle Scholar
  7. 7.
    Donoser M, Schmalstieg D (2014) Discrete-continuous gradient orientation estimation for faster image segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR), 2014, IEEE, pp 3158–3165Google Scholar
  8. 8.
    Kim TH, Lee KM, Lee SU (2013) Learning full pairwise affinities for spectral segmentation. IEEE Trans Pattern Anal Mach Intell 35(7):1690–1703CrossRefGoogle Scholar
  9. 9.
    Levine MD, Nazif AM (1985) Dynamic measurement of computer generated image segmentations. IEEE Trans Pattern Anal Mach Intell 2:155–164CrossRefGoogle Scholar
  10. 10.
    Maire M, Arbeláez P, Fowlkes C, Malik J (2008) Using contours to detect and localize junctions in natural images. In: IEEE conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE, pp 1–8Google Scholar
  11. 11.
    Malisiewicz T, Efros AA (2007) Improving spatial support for objects via multiple segmentations. British Machine Vision Conference (BMVC)Google Scholar
  12. 12.
    Meila M (2005) Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd international conference on machine learning (ICML-05), pp 577–584Google Scholar
  13. 13.
    Miao Q, Xu P, Liu T, Yang Y, Zhang J, Li W (2013) Linear feature separation from topographic maps using energy density and the shear transform. IEEE Trans Image Process 22(4):1548–1558MathSciNetCrossRefGoogle Scholar
  14. 14.
    Miao Q, Cao Y, Xia G, Gong M, Liu J, Song J (2015) Rboost: label noise-robust boosting algorithm based on a nonconvex loss function and the numerically stable base learners. IEEE Trans Neural Netw Learn Syst PP(99):1–1Google Scholar
  15. 15.
    Miao Q, Shi C, Pf X u, Yang M, Shi Y (2011) A novel algorithm of image fusion using shearlets. Opt Commun 284(6):1540–1547CrossRefGoogle Scholar
  16. 16.
    Sobel I, Feldman G (1968) A 3 × 3 isotropic gradient operator for image processing. In: a talk at the Stanford Artificial Project in, pp 271–272Google Scholar
  17. 17.
    Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 29(6):929–944CrossRefGoogle Scholar
  18. 18.
    Zhang H, Fritts JE, Goldman SA (2003) An entropy-based objective evaluation method for image segmentation. In: Electronic imaging 2004, international society for optics and photonics, pp 38–49Google Scholar
  19. 19.
    Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 110(2):260–280CrossRefGoogle Scholar
  20. 20.
    Zhang X, Xiao P, Feng X (2012) An unsupervised evaluation method for remotely sensed imagery segmentation. IEEE Geosci Remote Sens Lett 9(2):156–160CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Huizhou UniversityHuizhouPeople’s Republic of China
  2. 2.South China University of TechnologyGuangzhouPeople’s Republic of China

Personalised recommendations