Weakly supervised semantic segmentation with superpixel embedding

Frank Z. Xing, Erik Cambria, Win-Bin Huang, Yang Xu

Abstract: In this paper, we propose to use contexts of superpixels as a prior to improve semantic segmentation by the CRF framework. A graphical model is constructed on over-segmented images. Our main contribution is to take the concept of “superpixel embedding” into consideration, which is formalized as a potential item for optimizing the energy of the whole graph. We also introduce two ways of calculating this embedding potential. Experiments on several popular datasets, e.g., MRSC-21 and PASCAL VOC, illustrate that our approach enhances the performance of a previously proposed segmentation model without embedding. The accuracy results are comparable to some fully supervised methods.