  Image segmentation, one of the most critical vision tasks, has been studied
for many years. Most of the early algorithms are unsupervised methods, which
use hand-crafted features to divide the image into many regions. Recently,
owing to the great success of deep learning technology, CNNs based methods show
superior performance in image segmentation. However, these methods rely on a
large number of human annotations, which are expensive to collect. In this
paper, we propose a deep unsupervised method for image segmentation, which
contains the following two stages. First, a Superpixelwise Autoencoder
(SuperAE) is designed to learn the deep embedding and reconstruct a smoothed
image, then the smoothed image is passed to generate superpixels. Second, we
present a novel clustering algorithm called Deep Superpixel Cut (DSC), which
measures the deep similarity between superpixels and formulates image
segmentation as a soft partitioning problem. Via backpropagation, DSC
adaptively partitions the superpixels into perceptual regions. Experimental
results on the BSDS500 dataset demonstrate the effectiveness of the proposed
method.
