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Mean Shift A Robust Approach to Feature Space Analysis

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.

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Mean Shift A Robust Approach to Feature Space Analysis

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  1. Mean ShiftA Robust Approach toFeature Space Analysis Kalyan Sunkavalli 04/29/2008 ES251R

  2. An Example Feature Space

  3. An Example Feature Space

  4. An Example Feature Space Parametric Density Estimation?

  5. Mean Shift • A non-parametric technique for analyzing complex multimodal feature spaces and estimating the stationary points (modes) of the underlying probability density function without explicitly estimating it.

  6. Outline • Mean Shift • An intuition • Kernel Density Estimation • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  7. Outline • Mean Shift • An intuition • Kernel Density Estimation • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  8. Region of interest Intuitive Description Center of mass Mean Shift vector Slide Credit: Yaron Ukrainitz & Bernard Sarel Objective : Find the densest region Distribution of identical billiard balls

  9. Region of interest Intuitive Description Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical billiard balls

  10. Region of interest Intuitive Description Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical billiard balls

  11. Region of interest Intuitive Description Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical billiard balls

  12. Region of interest Intuitive Description Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical billiard balls

  13. Region of interest Intuitive Description Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical billiard balls

  14. Region of interest Intuitive Description Center of mass Objective : Find the densest region Distribution of identical billiard balls

  15. Outline • Mean Shift • An intuition • Kernel Density Estimation • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  16. Parametric Density Estimation The data points are sampled from an underlying PDF Estimate from data Assumed Underlying PDF Data Samples

  17. Non-parametric Density Estimation PDF value Data point density Assumed Underlying PDF Data Samples

  18. Non-parametric Density Estimation Assumed Underlying PDF Data Samples

  19. Parzen Windows Kernel Properties • Bounded • Compact support • Normalized • Symmetric • Exponential decay

  20. Kernels and Bandwidths • Kernel Types • Bandwidth Parameter (product of univariate kernels) (radially symmetric kernel)

  21. Various Kernels Epanechnikov Normal Uniform

  22. Outline • Mean Shift • An intuition • Kernel Density Estimation • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  23. Density Gradient Estimation Modes of the probability density Epanechnikov  Uniform Normal  Normal

  24. Mean Shift KDE Mean Shift Mean Shift Algorithm • compute mean shift vector • translate kernel (window) by mean shift vector

  25. Mean Shift • Mean Shift is proportional to the normalized density gradient estimate obtained with kernel • The normalization is by the density estimate computed with kernel

  26. Outline • Mean Shift • An intuition • Kernel Density Estimation • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  27. Properties of Mean Shift • Guaranteed convergence • Gradient Ascent algorithms are guaranteed to converge only for infinitesimal steps. • The normalization of the mean shift vector ensures that it converges. • Large magnitude in low-density regions, refined steps near local maxima  Adaptive Gradient Ascent. • Mode Detection • Let denote the sequence of kernel locations. • At convergence • Once gets sufficiently close to a mode of it will converge to the mode. • The set of all locations that converge to the same mode define the basin of attraction of that mode.

  28. Properties of Mean Shift • Smooth Trajectory • The angle between two consecutive mean shift vectors computed using the normal kernel is always less that 90° • In practice the convergence of mean shift using the normal kernel is very slow and typically the uniform kernel is used.

  29. Mode detection using Mean Shift • Run Mean Shift to find the stationary points • To detect multiple modes, run in parallel starting with initializations covering the entire feature space. • Prune the stationary points by retaining local maxima • Merge modes at a distance of less than the bandwidth. • Clustering from the modes • The basin of attraction of each mode delineates a cluster of arbitrary shape.

  30. Mode Finding on Real Data initialization tracks detected mode

  31. Mean Shift Clustering

  32. Outline • Mean Shift • Density Estimation • What is mean shift? • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  33. Joint Spatial-Range Feature Space • Concatenate spatial and range (gray level or color) information

  34. Discontinuity Preserving Smoothing

  35. Discontinuity Preserving Smoothing

  36. Discontinuity Preserving Smoothing

  37. Discontinuity Preserving Smoothing

  38. Outline • Mean Shift • Density Estimation • What is mean shift? • Derivation • Properties • Applications of Mean Shift • Discontinuity preserving Smoothing • Image Segmentation

  39. Clustering on Real Data

  40. Image Segmentation

  41. Image Segmentation

  42. Image Segmentation

  43. Image Segmentation

  44. Image Segmentation

  45. Acknowledgements • Mean shift: A robust approach toward feature space analysis. D Comaniciu, P Meer Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 24, No. 5. (2002), pp. 603-619. • http://www.caip.rutgers.edu/riul/research/papers.html • Slide credits: Yaron Ukrainitz & Bernard Sarel

  46. Thank You

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