Visual Clustering

Visual clustering aims to automatically group similar images or data points based on their visual features, facilitating efficient analysis of large, unlabeled datasets. Recent research emphasizes improving clustering accuracy and efficiency through novel algorithms like transformer-based models and quantum computing approaches, as well as incorporating user preferences and addressing issues like fairness and interpretability. These advancements are significant for various applications, including image retrieval, material classification in hyperspectral imaging, and personalized data exploration, by enabling more accurate, efficient, and user-friendly analysis of complex visual data.