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

Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization