Introduction to Basic and Deep Learning Based Methods for Unsupervised Image Segmentation

Abstract

Unsupervised image segmentation is an important technique in various research fields such as medical image processing. Basically, unsupervised image segmentation is based on some hand-crafted features and clustering pixels in a way that takes into account feature similarity and spatial continuity. In contrast, the authors proposed a method that applies unsupervised learning of convolutional neural networks (CNNs) to image segmentation. The proposed CNN estimates to which cluster each pixel in the input image belongs, as in a general supervised image segmentation task. However, it does not require any supervisory signals of pixel labels or network pre-training, and the network is trained only after the target image is input. In this paper, we describe the conventional basics of such unsupervised image segmentation, as well as the authorʼs proposed method using deep learning.