We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a recent multichannel multiphase Chan-Vese model, which is capable to extract useful information from multiple input images simultaneously. We implement a flexible multiclass segmentation method that divides a given image into $K$ different regions. We use convolutional neural networks (CNNs) targeting a pre-decomposition of the image. By subsequently minimising the segmentation functional, the final segmentation is obtained in a fully unsupervised manner. Special emphasis is given to the extraction of informative feature maps serving as a starting point for the segmentation. The initial results indicate that the proposed method is able to decompose and segment the different regions of various types of images, such as texture and medical images and compare its performance with another multiphase segmentation method.

翻译：我们提出一种无监督图像分割方法，该方法结合了变分能量泛函与深度卷积神经网络。变分部分基于近期提出的多通道多相位Chan-Vese模型，该模型能够同时从多幅输入图像中提取有用信息。我们实现了一种灵活的多类分割方法，可将给定图像划分为$K$个不同区域。通过使用卷积神经网络（CNN）对图像进行预分解，随后最小化分割泛函，最终以完全无监督的方式获得分割结果。特别强调了提取作为分割起点的信息性特征图。初步结果表明，所提方法能够对纹理图像、医学图像等多种类型图像的不同区域进行分解与分割，并将其性能与另一种多相位分割方法进行了比较。