Multimedia Tools and Applications

, Volume 75, Issue 18, pp 11417–11432 | Cite as

Histogram-based colour image fuzzy clustering algorithm

  • Hai-peng Chen
  • Xuan-Jing Shen
  • Jian-Wu Long


In this work, a histogram-based colour image fuzzy clustering algorithm is proposed for addressing the problem of low efficiency due to computational complexity and poor clustering performance. Firstly, the presented scheme constructs the red, green and blue (short for RGB) component histograms of a given colour image, each of which is pre-processed to preserve their smoothness. Secondly, the proposed algorithm multi-thresholds each component histogram, using some dominant valleys identified from a fast peak-valley location scheme in each global histogram. Thirdly, a new histogram is reconstructed by applying a histogram merging scheme to the RGB three-component histograms, and multi-thresholding this new histogram again using some dominant valleys obtained from the fast peak-valley location scheme. Thus, the proposed approach can easily identify the initialisation condition of cluster centroids and centroid number. Finally, we construct a new dataset composed of some pre-segmented small regions using the WaterShed algorithm, and the FCM (Fuzzy C-Means) algorithm is executed on this dataset, instead of on pixels, in combination with the initial cluster centroids. Experimental results have demonstrated that the proposed algorithm is more efficient than the DSRPCL (Distance Sensitive Rival Penalised Competitive Learning) algorithm and the HTFCM (Histogram Thresholding Fuzzy C-Means) algorithm with respect to run times and PRI (Probability Rand Index) values.


Colour image segmentation Histogram Clustering FCM algorithm 


  1. 1.
  2. 2.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRefGoogle Scholar
  3. 3.
    Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337MathSciNetCrossRefGoogle Scholar
  4. 4.
    Li Y, Sun J, Tang CK et al (2004) Lazy snapping. ACM Trans Graph 23(3):303–308MathSciNetCrossRefGoogle Scholar
  5. 5.
    Li CM, Huang R, Ding ZH et al (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Trans Image Process 20(7):2007–2016MathSciNetCrossRefGoogle Scholar
  6. 6.
    Long JW, Shen XJ, Chen HP (2012) Adaptive minimum error thresholding algorithm. Acta Autom Sin 38(7):1134–1144MathSciNetCrossRefGoogle Scholar
  7. 7.
    Long JW, Shen XJ, Chen HP (2012) Interactive document images thresholding segmentation algorithm based on images regions. J Comput Res Dev 49(7):1420–1431MathSciNetGoogle Scholar
  8. 8.
    Ma JW, Wang TJ (2006) A cost-function approach to rival penalized competitive learning (RPCL). IEEE Trans Syst Man Cybern B Cybern 36(4):722–737CrossRefGoogle Scholar
  9. 9.
    Mok PY, Huang HQ, Kwok YL et al (2012) A robust adaptive clustering analysis method for automatic identification of clusters. Pattern Recogn 45(8):3017–3033CrossRefGoogle Scholar
  10. 10.
    Peng B, Zhang L, Zhang D et al (2011) Image segmentation by iterated region merging with localized graph cuts. Pattern Recogn 44(10–11):2527–2538CrossRefGoogle Scholar
  11. 11.
    Peng B, Zhang L, Zhang D (2013) A survey of graph theoretical approaches to image segmentation. Pattern Recogn 46(3):1020–1038CrossRefGoogle Scholar
  12. 12.
    Sarkar S, Das S, Chaudhuri SS (2015) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution[J]. Pattern Recogn Lett 54(1):27–35CrossRefGoogle Scholar
  13. 13.
    Shen XJ, Long JW, Chen HP et al (2011) Otsu thresholding algorithm based on rebuilding and dimension reduction of the 3-Dimensional histogram. Acta Electron Sin 39(5):1108–1114Google Scholar
  14. 14.
    Tan KS, Isa NAM (2011) Color image segmentation using histogram thresholding-Fuzzy C-means hybrid approach. Pattern Recogn 44(1):1–15zbMATHCrossRefGoogle Scholar
  15. 15.
    Unnikrishnan R, Pantofru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 29(6):929–944CrossRefGoogle Scholar
  16. 16.
    Vantaram SR, Saber E (2012) Survey of contemporary trends in color image segmentation. J Electron Imaging 21(4):1–28CrossRefGoogle Scholar
  17. 17.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598CrossRefGoogle Scholar
  18. 18.
    Wei W, Shen XJ, Qian QJ (2011) An adaptive thresholding algorithm based on grayscale wave transformation for industrial inspection images. Acta Autom Sins 37(8):944–953Google Scholar
  19. 19.
    Yu ZD, Au OC, Zou RB et al (2010) An adaptive unsupervised approach toward pixel clustering and color image segmentation. Pattern Recogn 43(5):1889–1906zbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina
  3. 3.College of Computer Science and EngineeringChongqing University of TechnologyChongqingChina

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