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Mean Shift Segmentation
Raul Queiroz Feitosa
What is Mean shift ?
A tool for:
Finding modes in a set of data samples, manifesting an underlying probability density function (PDF) in RN .
PDF in feature space
Color space
Scale space
Actually any feature space you can conceive
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Basic IdeaObjective: Find the densest region
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Neighborhood
Mean shift vectorCenter of mass
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Computing the Mean shift Vector
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Computing the Mean shift Vector
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Kernel Functions
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Examples of kernel functions :
• Epanechnikov Kernel
• Uniform Kernel
• Normal Kernel
21 1
( ) 0 otherwise
E
cK
x xx
1( )
0 otherwiseU
cK
xx
21( ) exp
2NK c
x x
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Adaptive
Gradient
Ascent
• Automatic convergence speed – the mean shift
vector size depends on the gradient itself.
• Near maxima, the steps are small and refined
• Convergence is guaranteed for infinitesimal
steps only infinitely convergent,
(therefore set a lower bound)
Mean shift properties
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Modality analysis
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Tessellate the space with windows Run the procedure in parallel11/28/2019 Mean Shift Segmentation
Modality analysis
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Merge windows that end up near the same “peak” or mode
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• Cluster: all data points in the attraction basin of a mode.
• Attraction basin: the region for which all trajectories lead to
the same mode.
Mean shift clustering
Slide by Y. Ukrainitz & B. Sarel
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Mean shift clustering
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window size in
range domain
window size in
space domain
Mean shift segmentation
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Mean shift segmentation results
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From Comaniciu & Meer, 2002
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Mean shift segmentation results
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From Comaniciu & Meer, 2002
Mean shift segmentation results
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From Comaniciu & Meer, 2002
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Mean shift segmentation results
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From Comaniciu & Meer, 2002
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Pros & Cons
Pros
Does not assume spherical clusters
Window size has a physical meaning
Robust to outliers
Cons
Output depends on window size
Computationally expensive
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Exercise
Download the Color Clustering Methods using
Mean-Shift, Normalized-Cuts and KNN from here,
and experiment with with it.
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References
Ukrainitz, Y. & Bernard Sarel, B., slides avaiable in
http://www.wisdom.weizmann.ac.il/~vision/courses/2004_2/files/mean_shi
ft/mean_shift.ppt
Comaniciu, D. and Meer, P. (2002). Mean Shift: A Robust Approach
Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis
and Machine Intelligence, 24(5), pp. 603–619.
Kaftan, J.N., Bell, A.A., Aach, T. (2008). Mean Shift Segmentation
Evaluation of optimization Techniques, Proceedings of the Third
International Conference on Computer Vision Theory and Applications,
VISAPP 2008, pp. 365-374.
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Next Topic
Graph Based
Segmentation
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