Learning Based SLIC Superpixel Generation and Image Segmentation

Abstract

Superpixel generation is to cluster the pixels with similar features and plays an important role for image segmentation. Conventional superpixel generation methods are more meaningful, however, the learning based method can generate the superpixels directly from the segments in the ground truth and achieve even better performance. In this work, an advanced superpixel generation algorithm that combines the advantages of conventional methods and modern neural network techniques is proposed. In addition to colors and locations, we find that the feature generated by neural networks also provide useful information for superpixel assignment. Simulations show that, with the proposed superpixels, a much more precise segmentation result can be achieved.