  Image segmentation is the foundation of several computer vision tasks, where
pixel-wise knowledge is a prerequisite for achieving the desired target. Deep
learning has shown promising performance in supervised image segmentation.
However, supervised segmentation algorithms require a massive amount of data
annotated at a pixel level, thus limiting their applicability and scalability.
Therefore, there is a need to invest in unsupervised learning for segmentation.
This work presents an improved version of an unsupervised Convolutional Neural
Network (CNN) based algorithm that uses a constant weight factor to balance
between the segmentation criteria of feature similarity and spatial continuity,
and it requires continuous manual adjustment of parameters depending on the
degree of detail in the image and the dataset. In contrast, we propose a novel
dynamic weighting scheme that leads to a flexible update of the parameters and
an automatic tuning of the balancing weight between the two criteria above to
bring out the details in the images in a genuinely unsupervised manner. We
present quantitative and qualitative results on four datasets, which show that
the proposed scheme outperforms the current unsupervised segmentation
approaches without requiring manual adjustment.
