Unsupervised Image Segmentation using Mutual Mean-Teaching

TL;DR

This paper introduces an unsupervised image segmentation method based on the Mutual Mean-Teaching framework, which produces stable results and outperforms existing methods by employing a label alignment algorithm.

Contribution

The paper presents a novel unsupervised segmentation model using Mutual Mean-Teaching and a label alignment algorithm to improve stability and performance.

Findings

Achieves better segmentation performance than existing methods.

Produces stable results across various image types.

Utilizes Hungarian algorithm for label matching.

Abstract

Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision. Due to lack of prior knowledge, most of existing model usually need to be trained several times to obtain suitable results. To address this problem, we propose an unsupervised image segmentation model based on the Mutual Mean-Teaching (MMT) framework to produce more stable results. In addition, since the labels of pixels from two model are not matched, a label alignment algorithm based on the Hungarian algorithm is proposed to match the cluster labels. Experimental results demonstrate that the proposed model is able to segment various types of images and achieves better performance than the existing methods.