Improved Fast Gauss Transform and Efficient Kernel Density Estimation

  • Authors:
  • Changjiang Yang;Ramani Duraiswami;Nail A. Gumerov;Larry Davis

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
  • Year:
  • 2003

Quantified Score

Hi-index 0.01

Visualization

Abstract

Evaluating sums of multivariate Gaussians is a common computationaltask in computer vision and pattern recognition, including in thegeneral and powerful kernel density estimation technique. Thequadratic computational complexity of the summation is asignificant barrier to the scalability of this algorithm topractical applications. The fast Gauss transform (FGT) hassuccessfully accelerated the kernel density estimation to linearrunning time for low-dimensional problems. Unfortunately, the costof a direct extension of the FGT to higher-dimensional problemsgrows exponentially with dimension, making it impractical fordimensions above 3. We develop an improved fast Gauss transform toefficiently estimate sums of Gaussians in higher dimensions, wherea new multivariate expansion scheme and an adaptive spacesubdivision technique dramatically improve the performance. Theimproved FGT has been applied to the mean shift algorithm achievinglinear computational complexity. Experimental results demonstratethe efficiency and effectiveness of our algorithm.