End-to-end trainable network for superpixel and image segmentation

Abstract

Image segmentation and superpixel generation have been studied for many years, and they are still active research topics in computer vision. Although many advanced computer vision algorithms have been used for image segmentation and superpixel generation, there is no end-to-end trainable algorithm that generates superpixels and segment images simultaneously. We propose an end-to-end trainable network to solve this problem. We train a differentiable clustering algorithm module to produce accurate superpixels. Based on the generated superpixels, the superpixel pooling operation is performed to obtain superpixel features, and then we calculate the similarity of two adjacent superpixels. If the similarity is greater than the preset threshold, we merge the two superpixels. Finally, we get the segmented image. We conduct our experiments in the BSDS500 dataset and get good results.