Natural image segmentation plays an important role in the fields of image processing and computer vision. Image segmentation based on clustering is an important method in unsupervised image segmentation algorithms. But there are two problems with this type of approach. First, feature extraction is generally pixel-based, which results in poor segmentation results and boundary fitting. In order to solve this problem, it is proposed to introduce super pixel to be segmented image preprocessing. Second, the number of partitions is difficult to determine. Aiming at this problem, an energy difference based on mutual information is proposed, which can automatically determine the number of partitions. The experimental results on the standard database show that the proposed algorithm overcomes the above problems and achieves better experimental results.
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization
