  The usage of convolutional neural networks (CNNs) for unsupervised image
segmentation was investigated in this study. In the proposed approach, label
prediction and network parameter learning are alternately iterated to meet the
following criteria: (a) pixels of similar features should be assigned the same
label, (b) spatially continuous pixels should be assigned the same label, and
(c) the number of unique labels should be large. Although these criteria are
incompatible, the proposed approach minimizes the combination of similarity
loss and spatial continuity loss to find a plausible solution of label
assignment that balances the aforementioned criteria well. The contributions of
this study are four-fold. First, we propose a novel end-to-end network of
unsupervised image segmentation that consists of normalization and an argmax
function for differentiable clustering. Second, we introduce a spatial
continuity loss function that mitigates the limitations of fixed segment
boundaries possessed by previous work. Third, we present an extension of the
proposed method for segmentation with scribbles as user input, which showed
better accuracy than existing methods while maintaining efficiency. Finally, we
introduce another extension of the proposed method: unseen image segmentation
by using networks pre-trained with a few reference images without re-training
the networks. The effectiveness of the proposed approach was examined on
several benchmark datasets of image segmentation.
