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Hierarchical Layered Mean Shift Methods

  • Milan Šurkala
  • Karel Mozdřeň
  • Radovan Fusek
  • Eduard Sojka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)

Abstract

Many image processing tasks exist and segmentation is one of them. We are focused on the mean-shift segmentation method. Our goal is to improve its speed and reduce the over-segmentation problem that occurs with small spatial bandwidths. We propose new mean-shift method called Hierarchical Layered Mean Shift. It uses hierarchical preprocessing stage and stacking hierarchical segmentation outputs together to minimise the over-segmentation problem.

Keywords

layer segmentation image mean shift hierarchical 

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References

  1. 1.
    Carreira-Perpiñán, M.: Fast nonparametric clustering with Gaussian blurring mean-shift. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 153–160. ACM, New York (2006)Google Scholar
  2. 2.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)CrossRefGoogle Scholar
  3. 3.
    Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1197–1203 (1999)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: The variable bandwidth mean shift and data-driven scale selection. IEEE International Conference on Computer Vision 1, 438 (2001)Google Scholar
  6. 6.
    DeMenthon, D., Megret, R.: Spatio-Temporal Segmentation of Video by Hierarchical Mean Shift Analysis. Tech. Rep. LAMP-TR-090,CAR-TR-978,CS-TR-4388,UMIACS-TR-2002-68, University of Maryland, College Park (2002)Google Scholar
  7. 7.
    Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21(1), 32–40 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    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
  9. 9.
    Šurkala, M., Mozdřeň, K., Fusek, R., Sojka, E.: Layered mean shift methods. In: Pack, T. (ed.) SSVM 2013. LNCS, vol. 7893, pp. 465–476. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Vatturi, P., Wong, W.-K.: Category detection using hierarchical mean shift. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 847–856. ACM, New York (2009)Google Scholar
  11. 11.
    Šurkala, M., Mozdřeň, K., Fusek, R., Sojka, E.: Hierarchical blurring mean-shift. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2011. LNCS, vol. 6915, pp. 228–238. Springer, Heidelberg (2011), http://dl.acm.org/citation.cfm?id=2034246.2034270 CrossRefGoogle Scholar
  12. 12.
    Zhao, Q., Yang, Z., Tao, H., Liu, W.: Evolving mean shift with adaptive bandwidth: A fast and noise robust approach. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part I. LNCS, vol. 5994, pp. 258–268. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Milan Šurkala
    • 1
  • Karel Mozdřeň
    • 1
  • Radovan Fusek
    • 1
  • Eduard Sojka
    • 1
  1. 1.Faculty of Electrical Engieneering and InformaticsOstrava-PorubaCzech Republic

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