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Mean Shift and Its Application in Image Segmentation

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Book cover Innovations in Intelligent Image Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 339))

Abstract

Mean shift techniques have been demonstrated to be capable of estimating the local density gradients of similar image pixels. These gradient estimates are iteratively performed so that for all pixels similar pixels in corresponding images can be identified. In this chapter, we show how the application of a mean shift process can lead to improved image segmentation performance. We present several mean shift-based segmentation algorithms and demonstrate their superior performance against the classical approaches. Conclusions are drawn with respect to the effectiveness, efficiency and robustness of image segmentation using these approaches.

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References

  1. Argenziano, G., Soyer, H.P., De Giorgi, V.: Dermoscopy: A Tutorial. EDRE Medical Publishing & New Media (2002)

    Google Scholar 

  2. Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color- and texture-based image segmentation using EM and its application to content-based image retrieval. In: Proc. International Conference on Computer Vision, pp. 675–682 (1998)

    Google Scholar 

  3. Bezdek, J.: A convergence theorem for the fuzzy isodata clustering algorithms. IEEE Trans. Pattern Anal. Machine Intell. 2, 1–8 (1980)

    Article  MATH  Google Scholar 

  4. Bowyer, K., Kranenburg, C., Dougherty, S.: Edge detector evaluation using empirical ROC curves. Computer Vision and Image Understanding 84(1), 77–103 (2001)

    Article  MATH  Google Scholar 

  5. Bradski, G.R.: Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 2nd Quarter (1998)

    Google Scholar 

  6. Caselles, B., Coll, B., Miorel, J.-M.: A kanisza programme. Progr. Nonlinear Differential Equations Appl. 25, 35–55 (1996)

    Google Scholar 

  7. Cheng, T.W., Goldgof, D.B., Hall, L.O.: Fast fuzzy clustering. Fuzzy Sets Sys. 93, 49–56 (1998)

    Article  MATH  Google Scholar 

  8. Chuang, K., Tzeng, H., Chen, S., Wu, J., Chen, T.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imag. and Graph. 30, 9–15 (2006)

    Article  Google Scholar 

  9. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)

    Article  Google Scholar 

  10. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 142–149 (2000)

    Google Scholar 

  11. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience Publication, Hoboken (2000)

    Google Scholar 

  12. Jacobs, D.W., Weinshall, D., Gdalyahu, Y.: Classification with nonmetric distances: Image retrieval and class representation. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 583–600 (2000)

    Article  Google Scholar 

  13. Li, C., Liu, J., Fox, M.D.: Segmentation of edge preserving gradient vector flow: An approach toward automatically initializing and splitting of snakes. In: IEEE Conf. on Comput. Vis. Patter. Rec., pp. 162–167 (2005)

    Google Scholar 

  14. Liu, T., Zhou, H., Lin, F., Pang, Y., Wu, J.: Improving image segmentation by gradient vector flow and mean shift. Pattern Recognition Letters 29(1), 90–95 (2008)

    Article  Google Scholar 

  15. Makrogiannis, S., Economou, G., Fotopoulos, S.: A region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Trans. Syst., Man and Cyber., Part B 35(1), 44–53 (2005)

    Article  Google Scholar 

  16. 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: Proc. 8th Int’l Conf. Computer Vision, vol. 2, pp. 416–423 (2001)

    Google Scholar 

  17. Morris, O.J., Lee, J., Constantinides, A.G.: Graph theory for image analysis: An approach based on the shortest spanning tree. Proc. Inst. Electr. Eng. 133, 146–152 (1986)

    Google Scholar 

  18. Orbanz, P., Buhmann, J.M.: Nonparametric Bayesian image segmentation. Int. Journal of Computer Vision 77(1/3), 25–45 (2008)

    Article  Google Scholar 

  19. Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (2000)

    Article  Google Scholar 

  20. Staal, J.J., Niemeijer, A.M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. on Medical Imaging 23, 501–509 (2004)

    Article  Google Scholar 

  21. Szilagyi, L., Benyo, Z., Szilagyi, S.M., Adam, H.S.: Mr brain image segmentation using an enhanced fuzzy c-means algorithm. In: 25th Annual Information Conf. of IEEE EMBS, pp. 17–21 (2003)

    Google Scholar 

  22. Tao, W., Jin, H., Zhang, Y.: Color image segmentation based on mean shift and normalized cuts. IEEE Trans. Syst., Man and Cyber., Part B 37(5), 1382–1389 (2007)

    Article  Google Scholar 

  23. Venkateswarlu, N.B., Raju, P.S.V.S.K.: Fast isodata clustering algorithms. Pattern Recogn. 25(3), 335–342 (1992)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Wang, J., Thiesson, B., Xu, Y., Cohen, M.: Image and video segmentation by anisotropic kernel mean shift. In: Proc. European Conference on Computer Vision, pp. 238–249 (2004)

    Google Scholar 

  26. Wells III, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A.: Adaptive segmentation of mri data. In: Proc. of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine, pp. 59–69 (1995)

    Google Scholar 

  27. Witkin, A.P., Terzopoulos, D., Kass, M.: Signal matching through scale space. International Journal of Computer Vision 1(2), 133–144 (1987)

    Article  Google Scholar 

  28. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. on Image Processing 7(3), 359–369 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  29. Zhou, H., Liu, T., Hu, H., Pang, Y., Lin, F., Wu, J.: A hybrid framework for image segmentation. In: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 749–752 (2005)

    Google Scholar 

  30. Zhou, H., Schaefer, G., Celebi, M.E., Fei, M.: Bayesian image segmentation with mean shift. In: Proc. of IEEE Conference on Image Processing, pp. 2405–2408 (2009a)

    Google Scholar 

  31. Zhou, H., Schaefer, G., Liu, T., Lin, F.: Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm. Multimedia Tools and Applications 49(3), 447–462 (2010)

    Article  Google Scholar 

  32. Zhou, H., Schaefer, G., Sadka, A.H., Celebi, M.E.: Anisotropic mean shift based fuzzy c-means segmentation of dermoscopy images. IEEE J. Select. Topics in Sig. Proc. 3(1), 26–34 (2009b)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Queen’s University Belfast, Belfast, United Kingdom

    Huiyu Zhou

  2. Yucheng Technological Limited, Beijing, China

    Xun Wang

  3. Loughborough University, Loughborough, United Kingdom

    Gerald Schaefer

Authors
  1. Huiyu Zhou
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  2. Xun Wang
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  3. Gerald Schaefer
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Editor information

Editors and Affiliations

  1. Deputy Director for Scientific Researches Institute of Informatics, Wroclaw University of Technology , Wyb. Wyspianskiego 27, 51-370, Wroclaw, Poland

    Halina Kwaśnicka

  2. Founding Director of the KES Centre School of Electrical and Information Engineering, Knowledge-Based Engineering University of South Australia Adelaide , Mawson Lakes Campus, 5095, South Australia, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Zhou, H., Wang, X., Schaefer, G. (2011). Mean Shift and Its Application in Image Segmentation. In: Kwaśnicka, H., Jain, L.C. (eds) Innovations in Intelligent Image Analysis. Studies in Computational Intelligence, vol 339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17934-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-17934-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17933-4

  • Online ISBN: 978-3-642-17934-1

  • eBook Packages: EngineeringEngineering (R0)

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