Abstract
Mean Shift is a clustering algorithm based on kernel density estimation. Various extensions have been proposed to improve speed and quality.
References
Bradski GR (1998) Computer vision face tracking for use in a perceptual user interface. Intel Technol J Q2(Q2):214–219
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
Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577
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
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
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
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
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
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
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
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
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
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
Chapter history
-
Latest
Mean Shift- Published:
- 12 April 2023
DOI: https://doi.org/10.1007/978-1-4899-7502-7_532-2
-
Original
Mean Shift- Published:
- 10 June 2016
DOI: https://doi.org/10.1007/978-1-4899-7502-7_532-1