Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering
Abstract
Most superpixel methods are sensitive to noise and cannot control the superpixel number precisely. To solve these problems, in this paper, we propose a robust superpixel method called fuzzy simple linear iterative clustering (Fuzzy SLIC), which adopts a local spatial fuzzy C-means clustering and dynamic fuzzy superpixels. We develop a fast and precise superpixel number control algorithm called onion peeling (OP) algorithm. Fuzzy SLIC is insensitive to most types of noise, including Gaussian, salt and pepper, and multiplicative noise. The OP algorithm can control the superpixel number accurately without reducing much computational efficiency. In the validation experiments, we tested the Fuzzy SLIC and OP algorithm and compared them with state-of-the-art methods on the BSD500 and Pascal VOC2007 benchmarks. The experiment results show that our methods outperform state-of-the-art techniques in both noise-free and noisy environments.
Cite
@article{arxiv.1812.10932,
title = {Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering},
author = {Chong Wu and Jiangbin Zheng and Zhenan Feng and Houwang Zhang and Le Zhang and Jiawang Cao and Hong Yan},
journal= {arXiv preprint arXiv:1812.10932},
year = {2020}
}
Comments
12 pages, 14 figures. This paper has been accepted as a Transactions Paper for publication by IEEE Transactions on Circuits and Systems for Video Technology