W-Net: A Deep Model for Fully Unsupervised Image Segmentation

TL;DR

This paper introduces W-Net, a novel deep architecture for fully unsupervised image segmentation that combines two convolutional networks into an autoencoder, jointly optimizing reconstruction and segmentation quality.

Contribution

The paper presents a new unsupervised segmentation model that leverages a dual-network autoencoder architecture inspired by supervised methods, without requiring labeled data.

Findings

Outperforms existing unsupervised segmentation methods on Berkeley dataset

Joint optimization of autoencoder reconstruction and normalized cut improves segmentation quality

Postprocessing with CRF smoothing enhances the final segmentation results

Abstract

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.