  While image segmentation is crucial in various computer vision applications,
such as autonomous driving, grasping, and robot navigation, annotating all
objects at the pixel-level for training is nearly impossible. Therefore, the
study of unsupervised image segmentation methods is essential. In this paper,
we present a pixel-level clustering framework for segmenting images into
regions without using ground truth annotations. The proposed framework includes
feature embedding modules with an attention mechanism, a feature statistics
computing module, image reconstruction, and superpixel segmentation to achieve
accurate unsupervised segmentation. Additionally, we propose a training
strategy that utilizes intra-consistency within each superpixel,
inter-similarity/dissimilarity between neighboring superpixels, and structural
similarity between images. To avoid potential over-segmentation caused by
superpixel-based losses, we also propose a post-processing method. Furthermore,
we present an extension of the proposed method for unsupervised semantic
segmentation. We conducted experiments on three publicly available datasets
(Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff
dataset) to demonstrate the effectiveness of the proposed framework. The
experimental results show that the proposed framework outperforms previous
state-of-the-art methods.
