While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult to obtain. In this paper, we revisit the problem of purely unsupervised image segmentation and propose a novel deep architecture for this problem. We borrow recent ideas from supervised semantic segmentation methods, in particular by concatenating two fully convolutional networks together into an autoencoder--one for encoding and one for decoding. The encoding layer produces a k-way pixelwise prediction, and both the reconstruction error of the autoencoder as well as the normalized cut produced by the encoder are jointly minimized during training. When combined with suitable postprocessing involving conditional random field smoothing and hierarchical segmentation, our resulting algorithm achieves impressive results on the benchmark Berkeley Segmentation Data Set, outperforming a number of competing methods.

本文提出了一种新颖的深度架构用于纯无监督图像分割，通过自编码器将两个全卷积网络进行串联，并在训练期间联合最小化自编码器的重构误差和编码器产生的标准化割。结合适当的后处理，包括条件随机场平滑和分层分割，该算法在基准伯克利分割数据集上取得了出色的结果，优于许多竞争方法。

W-Net: 一种用于完全无监督图像分割的深度模型