Skip to main content

Mean Shift

  • Living reference work entry
  • Latest version View entry history
  • First Online:
Encyclopedia of Machine Learning and Data Science
  • 18 Accesses

Abstract

Mean Shift is a clustering algorithm based on kernel density estimation. Various extensions have been proposed to improve speed and quality.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  • Bradski GR (1998) Computer vision face tracking for use in a perceptual user interface. Intel Technol J Q2(Q2):214–219

    Google Scholar 

  • Cetingul HE, Vidal R (2009) Intrinsic mean shift for clustering on stiefel and grassmann manifolds. In: IEEE conference on computer vision and pattern recognition (CVPR 2009), Miami, pp 1896–1902

    Google Scholar 

  • Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799

    Article  Google Scholar 

  • Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  • Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  Google Scholar 

  • Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21(1):32–40

    Article  MathSciNet  MATH  Google Scholar 

  • Georgescu B, Shimshoni I, Meer P (2003) Mean shift based clustering in high dimensions: a texture classification example. In: Proceedings of ninth IEEE international conference on computer vision 2003, Nice, vol 1, pp 456–463

    Google Scholar 

  • Paris S, Durand F (2007) A topological approach to hierarchical segmentation using mean shift. In: IEEE conference on computer vision and pattern recognition (CVPR 2007), Minneapolis, pp 1–8

    Google Scholar 

  • Sheikh YA, Khan EA, Kanade T (2007) Mode-seeking by medoidshifts. In: IEEE 11th international conference on computer vision (ICCV 2007), Rio de Janeiro, pp 1–8

    Google Scholar 

  • Subbarao R, Meer P (2006) Nonlinear mean shift for clustering over analytic manifolds. In: IEEE computer society conference on computer vision and pattern recognition (CVPR 2006), vol 1, pp 1168–1175

    Google Scholar 

  • Tuzel O, Subbarao R, Meer P (2005) Simultaneous multiple 3D motion estimation via mode finding on lie groups. In: Tenth IEEE international conference on computer vision (ICCV 2005), vol 1, pp 18–25

    Google Scholar 

  • Vedaldi A, Soatto S (2008) Quick shift and kernel methods for mode seeking. In: Forsyth D, Torr P, Zisserman A (eds) Computer vision ECCV 2008. Lecture notes in computer science, vol 5305. Springer, Berlin/Heidelberg, pp 705–718

    Chapter  Google Scholar 

  • Yilmaz A (2007) Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection. In: IEEE conference on computer vision and pattern recognition (CVPR 2007), Minneapolis, pp 1–6

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Jin, X., Han, J. (2023). Mean Shift. In: Phung, D., Webb, G.I., Sammut, C. (eds) Encyclopedia of Machine Learning and Data Science. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7502-7_532-2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_532-2

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7502-7

  • Online ISBN: 978-1-4899-7502-7

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Mean Shift
    Published:
    12 April 2023

    DOI: https://doi.org/10.1007/978-1-4899-7502-7_532-2

  2. Original

    Mean Shift
    Published:
    10 June 2016

    DOI: https://doi.org/10.1007/978-1-4899-7502-7_532-1