  The Chan-Vese (CV) model is a classic region-based method in image
segmentation. However, its piecewise constant assumption does not always hold
for practical applications. Many improvements have been proposed but the issue
is still far from well solved. In this work, we propose an unsupervised image
segmentation approach that integrates the CV model with deep neural networks,
which significantly improves the original CV model's segmentation accuracy. Our
basic idea is to apply a deep neural network that maps the image into a latent
space to alleviate the violation of the piecewise constant assumption in image
space. We formulate this idea under the classic Bayesian framework by
approximating the likelihood with an evidence lower bound (ELBO) term while
keeping the prior term in the CV model. Thus, our model only needs the input
image itself and does not require pre-training from external datasets.
Moreover, we extend the idea to multi-phase case and dataset based unsupervised
image segmentation. Extensive experiments validate the effectiveness of our
model and show that the proposed method is noticeably better than other
unsupervised segmentation approaches.
