A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection

TL;DR

This paper introduces a robust centroid update technique for clustering-based superpixel methods and a new superpixel-based edge detection approach, significantly improving noise robustness and edge detection accuracy.

Contribution

It proposes a novel centroid update approach to enhance noise robustness in clustering-based superpixels and a new edge detection method that outperforms classical techniques.

Findings

Enhanced superpixel performance in noisy environments

Outperforms classical edge detection methods

Significant improvement on BSD500 dataset

Abstract

Superpixel is widely used in image processing. And among the methods for superpixel generation, clustering-based methods have a high speed and a good performance at the same time. However, most clustering-based superpixel methods are sensitive to noise. To solve these problems, in this paper, we first analyze the features of noise. Then according to the statistical features of noise, we propose a novel centroid update approach to enhance the robustness of clustering-based superpixel methods. Besides, we propose a novel superpixel-based edge detection method. The experiments on BSD500 dataset show that our approach can significantly enhance the performance of clustering-based superpixel methods in noisy environment. Moreover, we also show that our proposed edge detection method outperforms other classical methods.