Unsupervised Image Segmentation

Unsupervised image segmentation aims to automatically partition images into meaningful regions without relying on manually labeled data, a crucial step for many computer vision applications where labeled datasets are scarce or expensive to obtain. Recent research focuses on leveraging deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers, often combined with graph neural networks (GNNs) or Gaussian mixture models (GMMs), to learn effective image representations and perform clustering or segmentation. These methods often incorporate techniques like self-supervised learning, superpixel generation, and iterative refinement to improve segmentation accuracy and efficiency. Advances in this field have significant implications for various domains, including medical imaging, remote sensing, and autonomous systems, by enabling automated analysis of large, unlabeled image datasets.