Accelerating Mean Shift Segmentation Algorithm on Hybrid CPU/GPU Platforms

Abstract. Image segmentation is a very important step in many GIS applications. Mean shift is an advanced and versatile technique for clustering-based segmentation, and is favored in many cases because it is non-parametric. However, mean shift is very computationally intensive com-pared with other simple methods such as k-means. In this work, we present a hybrid design of mean shift algorithm on a computer platform consisting of both CPUs and GPUs. By taking advantages of the mas-sive parallelism and the advanced memory hierarchy on Nvidia’s Fermi GPU, the hybrid design achieves a 20 × speedup compared with the pure CPU implementation when dealing with images bigger than 1024×1024 pixels