GPU-accelerated Faster Mean Shift with euclidean distance metrics
Dept. of Biomedical Engineering, Tufts University, Medford, MA 02155, USA
arXiv:2112.13891 [cs.CV], (27 Dec 2021)
@misc{you2021gpuaccelerated,
title={GPU-accelerated Faster Mean Shift with euclidean distance metrics},
author={Le You and Han Jiang and Jinyong Hu and Chorng Chang and Lingxi Chen and Xintong Cui and Mengyang Zhao},
year={2021},
eprint={2112.13891},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the mean-shift algorithm is restricted by its huge computational resource cost. In previous research[10], we proposed a novel GPU-accelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based mean-shift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed. (Plug-and-play model is available)
January 2, 2022 by hgpu
