  We propose an unsupervised superpixel segmentation method by optimizing a
randomly-initialized convolutional neural network (CNN) in inference time. Our
method generates superpixels via CNN from a single image without any labels by
minimizing a proposed objective function for superpixel segmentation in
inference time. There are three advantages to our method compared with many of
existing methods: (i) leverages an image prior of CNN for superpixel
segmentation, (ii) adaptively changes the number of superpixels according to
the given images, and (iii) controls the property of superpixels by adding an
auxiliary cost to the objective function. We verify the advantages of our
method quantitatively and qualitatively on BSDS500 and SBD datasets.
