  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.
