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：我们建议采用一种不受监督的图像分割法,将变化能量功能和深层进化神经网络结合起来。变式部分以最近的多通道多阶段Chan-Vese模型为基础,该模型能够同时从多个输入图像中提取有用信息。我们采用了一种灵活的多级分割法,将给定图像分为不同的地区,将给定图像分为不同的地区。我们使用进化神经网络(CNNs)针对图像的预解体。随后,通过最小化分解功能,最终分解以完全不受监督的方式获得。特别强调提取信息性地貌图作为分解的起点。初步结果显示,拟议的方法能够分解和分解各种图像的不同区域,例如纹理和医学图像,并将其性能与另一种多阶段分割法进行比较。