ML-Mean Shift Algorithm

Mean-shift: Issues

Speedups

  • Binned estimation
  • Fast search of neighbors
  • Update each window in each iteration (faster convergence)

Other Tricks

  • Use kNN to determine window sizes adaptively

Pros

  • Good general-practice segmentation
  • Flexible in number and shape of regions
  • Robust to outliers

Cons

  • Have to choose kernel size in advance
  • Not suitable for high-dimensional features

When to Use It

  • Oversegmentation
  • Multiple segmentations
  • Tracking, clustering, filtering applications
Table Of Contents
ML-Mean Shift Algorithm
Mean-shift: Issues
Speedups
Other Tricks
Pros
Cons
When to Use It