Online Clustering

Online clustering is an unsupervised learning method designed to handle image sets in the form of data streams, enabling the assignment of clusters to new images without the need to access the entire dataset. This method achieves real-time classification of unlabeled images by dynamically updating model parameters, thereby maintaining efficiency and accuracy in continuously changing data environments. Online clustering has significant application value in computer vision, as it can adapt to large-scale, high-dimensional image data streams and support real-time analysis and decision-making.