Acceleration Strategies for Gaussian Mean-Shift Image Segmentation

Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2 ), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM?Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude. The me...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Miguel Á. Carreira-Perpiñán
• Keywords: Computer Vision, CVPR 2006, Density Estimate P(x, Gaussian Kernel Density, Hill-climbing Algorithm, Kernel Density Estimate, Mean-shift Algorithm