Unsupervised Segmentation

Unsupervised image segmentation aims to automatically partition images into meaningful regions without relying on labeled training data, a significant challenge in computer vision and medical imaging. Current research focuses on leveraging pre-trained models (like Vision Transformers and CLIP) and employing techniques such as graph neural networks, clustering algorithms (e.g., k-means, expectation-maximization), and generative models (e.g., diffusion models) to achieve this. These methods are being applied across diverse domains, including pathology, medical imaging, and remote sensing, to address the limitations of supervised approaches where labeled data is scarce or expensive to obtain. Successful unsupervised segmentation techniques promise to significantly improve the efficiency and accessibility of image analysis in various fields.