# Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering

## TL;DR

Fuzzy SLIC introduces a noise-robust superpixel segmentation method using fuzzy clustering and a novel superpixel number control algorithm, outperforming existing techniques on standard benchmarks.

## Contribution

The paper presents Fuzzy SLIC, a superpixel method combining fuzzy clustering with a new control algorithm for precise superpixel count, enhancing noise robustness and efficiency.

## Key findings

- Outperforms state-of-the-art methods on BSD500 and Pascal VOC2007.
- Robust against various noise types including Gaussian and salt-and-pepper.
- Accurately controls superpixel number without significant computational cost.

## 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.