Deep Gaussian mixture model for unsupervised image segmentation

arXiv:2404.12252v1 Announce Type: new
Abstract: The recent emergence of deep learning has led to a great deal of work on designing supervised deep semantic segmentation algorithms. As in many tasks sufficient pixel-level labels are very difficult to obtain, we propose a method which combines a Gaussian mixture model (GMM) with unsupervised deep learning techniques. In the standard GMM the pixel values with each sub-region are modelled by a Gaussian distribution. In order to identify the different regions, the parameter vector …

abstract algorithms arxiv cs.cv deal deep learning designing emergence image labels pixel segmentation semantic tasks type unsupervised work