Pixel Level Clustering

Pixel-level clustering aims to group individual pixels in images into meaningful segments without relying on pre-labeled data, enabling unsupervised image segmentation and analysis. Current research focuses on improving clustering accuracy and efficiency through techniques like contrastive learning, which leverages similarities and differences between pixels or superpixels (groups of pixels), and diffusion geometry, which incorporates spatial relationships between pixels to enhance spectral reconstruction and clustering. These advancements are crucial for analyzing large datasets like hyperspectral images, impacting fields such as remote sensing, medical imaging, and autonomous systems by enabling automated feature extraction and object recognition.