  Unsupervised image segmentation is an important task in many real-world
scenarios where labelled data is of scarce availability. In this paper we
propose a novel approach that harnesses recent advances in unsupervised
learning using a combination of Mutual Information Maximization (MIM), Neural
Superpixel Segmentation and Graph Neural Networks (GNNs) in an end-to-end
manner, an approach that has not been explored yet. We take advantage of the
compact representation of superpixels and combine it with GNNs in order to
learn strong and semantically meaningful representations of images.
Specifically, we show that our GNN based approach allows to model interactions
between distant pixels in the image and serves as a strong prior to existing
CNNs for an improved accuracy. Our experiments reveal both the qualitative and
quantitative advantages of our approach compared to current state-of-the-art
methods over four popular datasets.
