TY - CONF T1 - Improved fast gauss transform and efficient kernel density estimation T2 - Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on Y1 - 2003 A1 - Yang,C. A1 - Duraiswami, Ramani A1 - Gumerov, Nail A. A1 - Davis, Larry S. KW - adaptive KW - algorithm;multivariate KW - complexity;computer KW - complexity;Gaussian KW - computational KW - density KW - estimation;mean KW - expansion KW - Gauss KW - processes;computational KW - recognition;quadratic KW - scheme;pattern KW - shift KW - space KW - subdivision KW - technique;computer KW - theory; KW - transform;kernel KW - vision;estimation KW - vision;fast AB - Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm. JA - Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on M3 - 10.1109/ICCV.2003.1238383 ER -