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Mean Shift A Robust Approach to Feature Space Analysis Kalyan Sunkavalli 04/29/2008 ES251R
An Example Feature Space
An Example Feature Space
An Example Feature Space Parametric Density Estimation?
Mean Shift ,[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region Slide Credit: Yaron Ukrainitz & Bernard Sarel
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Mean Shift vector Objective : Find the densest region
Intuitive Description Distribution of identical billiard balls Region of interest Center of mass Objective : Find the densest region
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Assumed Underlying PDF Estimate from data Data Samples Parametric Density Estimation The data points are sampled from an underlying PDF
Assumed Underlying PDF Data Samples Data point density Non-parametric Density Estimation PDF value
Assumed Underlying PDF Data Samples Non-parametric Density Estimation
Parzen Windows ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Kernels and Bandwidths ,[object Object],[object Object],(product of univariate kernels) (radially symmetric kernel)
Various Kernels Epanechnikov Normal Uniform
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Density Gradient Estimation Epanechnikov  Uniform Normal  Normal Modes of the probability density
Mean Shift KDE Mean Shift Mean Shift Algorithm ,[object Object],[object Object]
Mean Shift ,[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of Mean Shift ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of Mean Shift ,[object Object],[object Object],[object Object]
Mode detection using Mean Shift ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mode Finding on Real Data initialization detected mode tracks
Mean Shift Clustering
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Joint Spatial-Range Feature Space ,[object Object]
Discontinuity Preserving Smoothing
Discontinuity Preserving Smoothing
Discontinuity Preserving Smoothing
Discontinuity Preserving Smoothing
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering on Real Data
Image Segmentation
Image Segmentation
Image Segmentation
Image Segmentation
Image Segmentation
Acknowledgements ,[object Object],[object Object],[object Object]
Thank You

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