Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster Academic Article uri icon

abstract

  • © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. The mean shift image segmentation algorithm is very computation-intensive. To address the need to deal with a large number of remote sensing (RS) image segmentations in real-world applications, this study has investigated the parallelization of the mean shift algorithm on a single graphics processing unit (GPU) and a task-scheduling method with message passing interface (MPI)+OpenCL programming model on a GPU cluster platform. This paper presents the test results of the parallel mean shift image segmentation algorithm on Shelob, a GPU cluster platform at Louisiana State University, with different datasets and parameters. The experimental results show that the proposed parallel algorithm can achieve good speedups with different configurations and RS data and can provide an effective solution for RS image processing on a GPU cluster.

author list (cited authors)

  • Huang, F., Chen, Y., Li, L. i., Zhou, J. i., Tao, J., Tan, X., & Fan, G.

citation count

  • 9

publication date

  • February 2018