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Multimedia Tools and Applications

, Volume 78, Issue 22, pp 31347–31364 | Cite as

Image segmentation via multi dimensional color transform and consensus based region merging

  • Zubair Khan
  • Jie YangEmail author
Article
  • 75 Downloads

Abstract

Most natural scene real world images contain a lot of variations and produce a set of complex information. This complex information representation inducts difficulty in separating focal objects in the image. In this paper, hybrid cues are determined to efficiently separate the foreground objects from image background. The segmentation approach presented in our paper consists of two steps: 1) Production of clustering based super pixels 2) Consensus based optimal region merging process. First, input image is processed by mean shift as a noise removal step, then FCM clustering is employed to cluster pixels by utilising features based on extended color space transformations, following that final region labelling is done in terms of superpixel spatial connectivity. Second, hybrid cues are calculated as a tool for similarity measurement between regions, and a Consensus based region merging process is implemented by adjacent region similarity comparison with the standard deviation serving as a merging threshold, producing final segmentation. Experiments are conducted on Berkeley Segmentation Database and segmented images verify the efficiency of our approach in Natural Scene images.

Keywords

Unsupervised color image segmentation FCM Superpixels Multi feature aproach Hybrid cues 

Notes

Acknowledgments

This research is partly supported by NSFC, China (No:61572315) and Committee of Science and Technology, Shanghai, China (No:17JC1403000).

References

  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S et al (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282CrossRefGoogle Scholar
  2. 2.
    Bertelli L, Sumengen B, Manjunath BS, Gibou F (2008) A variational framework for multiregion pairwise-similarity-based image segmentation. IEEE Trans Pattern Anal Mach Intell 30(8):1400–1414CrossRefGoogle Scholar
  3. 3.
    Bo P, Zhang L, Zhang D (2011) Automatic image segmentation by dynamic region merging. IEEE Trans Image Process 20(12):3592–3605MathSciNetCrossRefGoogle Scholar
  4. 4.
    Carandell J, Garrido L, Igual L (2018) Cage active contours for image warping and morphing. EURASIP J Image Video Process 2018(1):10CrossRefGoogle Scholar
  5. 5.
    Chen L, Chen CLP, Lu M (2011) A multiple-kernel fuzzy c-means algorithm for image segmentation. IEEE Trans Syst Man Cybern B (Cybernetics) 41(5):1263–1274CrossRefGoogle Scholar
  6. 6.
    Cheng H-D, Jiang XH, Wang J (2002) Color image segmentation based on homogram thresholding and region merging. Pattern Recogn 35(2):373–393CrossRefGoogle Scholar
  7. 7.
    Cho SI, Kang S-J, Kim YH (2014) Human perception-based image segmentation using optimising of colour quantisation. IET Image Process 8(12):761–770CrossRefGoogle Scholar
  8. 8.
    Comaniciu D, Meer P (2002) Mean shift: A robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619CrossRefGoogle Scholar
  9. 9.
    Deng Yining, Manjunath B S (2001) Unsupervised segmentation of color-texture regions in images and video. IEEE Trans Pattern Anal Mach Intell 23(8):800–810CrossRefGoogle Scholar
  10. 10.
    Estrada FJ, Jepson AD (2005) Quantitative evaluation of a novel image segmentation algorithm. In: IEEE computer society conference on computer vision & pattern recognitionGoogle Scholar
  11. 11.
    Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181CrossRefGoogle Scholar
  12. 12.
    Han J, Quan R, Zhang D, Nie F (2018) Robust object co-segmentation using background prior. IEEE Trans Image Process 27(4):1639–1651MathSciNetCrossRefGoogle Scholar
  13. 13.
    Haralick RM, Shapiro LG (1985) Image segmentation techniques. In: Applications of artificial intelligence II, vol 548, pp 2–10. International Society for Optics and PhotonicsGoogle Scholar
  14. 14.
    Hettiarachchi R, Peters JF (2017) Voronoï region-based adaptive unsupervised color image segmentation. Pattern Recogn 65:119–135CrossRefGoogle Scholar
  15. 15.
    Ibrahim MT, Khan TM, Khan MA, Guan L (2010) Automatic segmentation of pupil using local histogram and standard deviation. In: Visual communications and image processing 2010, vol 7744, pp 77442S. International Society for Optics and PhotonicsGoogle Scholar
  16. 16.
    Kanezaki A (2018) Unsupervised image segmentation by backpropagation. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1543–1547. IEEEGoogle Scholar
  17. 17.
    Khan Z, Ni J, Fan X, Shi P (2017) An improved k-means clustering algorithm based on an adaptive initial parameter estimation procedure for image segmentation. Int J Innovative Comput Inf Control 13(5):1509–1525Google Scholar
  18. 18.
    Khelifi L, Mignotte M (2017) A novel fusion approach based on the global consistency criterion to fusing multiple segmentations. IEEE Trans Syst Man Cybern Syst 47(9):2489–2502Google Scholar
  19. 19.
    Kim J, Han D, Tai Y-W, Kim J (2014) Salient region detection via high-dimensional color transform. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 883–890Google Scholar
  20. 20.
    Kim J, Han D, Tai Y-W, Kim J (2016) Salient region detection via high-dimensional color transform and local spatial support. IEEE Trans Image Process 25(1):9–23MathSciNetCrossRefGoogle Scholar
  21. 21.
    Li Z, Chen J (2015) Superpixel segmentation using linear spectral clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1356–1363Google Scholar
  22. 22.
    Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12):3243MathSciNetCrossRefGoogle Scholar
  23. 23.
    Loesdau M, Chabrier S, Gabillon A (2014) Hue and saturation in the rgb color space. In: International conference on image and signal processing, pp 203–212. SpringerGoogle Scholar
  24. 24.
    Maggio E, Cavallaro A (2005) Multi-part target representation for color tracking. In: ICIP 2005 IEEE international conference on image processing, 2005, vol 1, pp I–729. IEEEGoogle Scholar
  25. 25.
    Makrogiannis S, Economou G, Fotopoulos S, Bourbakis NG (2005) Segmentation of color images using multiscale clustering and graph theoretic region synthesis. IEEE Trans Syst Man Cybern Part A Syst Hum 35(2):224–238CrossRefGoogle Scholar
  26. 26.
    Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 8th IEEE international conference on computer vision, 2001. ICCV Proceedings, vol 2, pp 416–423. IEEEGoogle Scholar
  27. 27.
    Meilă M (2005) Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd international conference on Machine learning, pp 577–584. ACMGoogle Scholar
  28. 28.
    Mignotte M (2008) Segmentation by fusion of histogram-based k-means clusters in different color spaces. IEEE Trans Image Process 17(5):780–787MathSciNetCrossRefGoogle Scholar
  29. 29.
    Moore AP, Prince SJD, Warrell J (2010) “lattice cut”-constructing superpixels using layer constraints. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 2117–2124. IEEEGoogle Scholar
  30. 30.
    Mourchid Y, El Hassouni M, Cherifi H (2019) A general framework for complex network-based image segmentation. Multimedia Tools and ApplicationsGoogle Scholar
  31. 31.
    Omer I, Werman M (2004) Color lines: Image specific color representation. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004., vol 2, pp II–II. IEEEGoogle Scholar
  32. 32.
    Rhyne T-M (2012) Applying color theory to digital media and visualization. In: ACM SIGGRAPH 2012 courses, SIGGRAPH ’12. ACM, New York, pp 1:1–1:82Google Scholar
  33. 33.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  34. 34.
    Sima H, Guo P, Zou Y, Wang Z, Xu M (2018) Bottom-up merging segmentation for color images with complex areas. IEEE Trans Syst Man Cybern Syst 48(3):354–365CrossRefGoogle Scholar
  35. 35.
    Stutz D (2015) Superpixel segmentation: An evaluation. In: German conference on pattern recognition, pp 555–562. SpringerGoogle Scholar
  36. 36.
    Unnikrishnan Ranjith, Pantofaru Caroline, Hebert Martial (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 29(6):929–944CrossRefGoogle Scholar
  37. 37.
    Wang X, Tang Y, Masnou S, Chen L (2015) A global/local affinity graph for image segmentation. IEEE Trans Image Process 24(4):1399–1411MathSciNetCrossRefGoogle Scholar
  38. 38.
    Xia X, Kulis B (2017) W-net: A deep model for fully unsupervised image segmentation. arXiv:1711.08506
  39. 39.
    Xie Y, Lu H, Yang M-H (2013) Bayesian saliency via low and mid level cues. IEEE Trans Image Process 22(5):1689–1698MathSciNetCrossRefGoogle Scholar
  40. 40.
    Yang AY, Wright J, Ma Y, Sastry SS (2008) Unsupervised segmentation of natural images via lossy data compression. Comput Vis Image Underst 110(2):212–225CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina

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