GMMSP on GPU

Abstract

Superpixel segmentation is a fundamental task in computer vision. Existing works contribute to superpixel segmentation either by improving segmentation accuracy or by reducing execution time. The former modifies existing models or develops new models to improve accuracy. The latter accelerates existing implementations or reduces algorithm complexity to improve execution rate. This work falls into the second category. Recently, a superpixel algorithm using Gaussian mixture model (GMMSP) achieves state-of-the-art performance in accuracy. After exploring this algorithm, we reached new conclusions on GMMSP that unlock potential concerning fine-grain parallelism implementation. We implement GMMSP with CUDA and make it run on GPUs. Experiments are conducted to validate the consistency between CPU and GPU implementations and to evaluate the performance of our implementation with respect to a serial and an OpenMP implementation. When we consider a full implementation with a postprocessing step executed on CPU to guarantee connectivity constraint, the proposed implementation achieves a speedup of 21× compared to the OpenMP implementation for images of size 240 × 320, using NVIDIA GTX 1080. It is also mentionable that we achieve a performance of over 1000 FPS on GTX 1080 (speedup of 77× compared to the OpenMP implementation) if the connectivity constraint is not included.