Abstract
Mean Shift is a clustering algorithm based on kernel density estimation. Various extensions have been proposed to improve speed and quality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
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, MN, 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, MN, pp 1–6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
Jin, X., Han, J. (2017). Mean Shift. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_532
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_532
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering