The Variable Bandwidth Mean Shift and Data-Driven Scale Selection
Dorin Comaniciu, Visvanathan Ramesh and Peter Meer(*)
Imaging and Visualization Department
Siemens Corporate Research
Princeton, NJ 08540
(*)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA
We present two solutions for the scale selection problem in computer
vision. The first one is completely nonparametric and is based on the
the adaptive estimation of the normalized density gradient. Employing
the sample point estimator, we define the Variable Bandwidth Mean
Shift, prove its convergence, and show its superiority over the fixed
bandwidth procedure. The second technique has a semiparametric nature
and imposes a local structure on the data to extract reliable scale
information. The local scale of the underlying density is taken as
the bandwidth which maximizes the magnitude of the normalized mean
shift vector. Both estimators provide practical tools for autonomous
image and quasi real-time video analysis and several examples are
shown to illustrate their effectiveness.
8th International Conference on Computer Vision ,
Vancouver, BC, Canada, July 2001, vol. I, 438-445.
Return to Research: Robust analysis of visual data
