Deep Gaussian mixture model for unsupervised image segmentation

Understand

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 that minimizes the negative log-likelihood (NLL) function regarding the GMM has to be approximated.