A Super-pixel based Method for Instance Segmentation Post-processing

We present a simple post-processing method for object instance segmentation. Instances segment images into parts with rich semantics while less texture consistency. Superpixels segment images into parts with great texture consistency while less semantics. We design a method, joining super-pixel to the instance segmentation workflow, in order to enhance the instance segmentation results. The workflow is, firstly calling a certain instance segmentation method (for example, Mask Region Convolutional Neural Network (R-CNN), Mask R-CNN) on the image to get prediction masks preliminary; then utilizing super-pixels as the assistant information to modify the prediction masks; and finally obtaining the better segmentation results. Our method is train-free, while it can refine the instance segmentation masks. Our experiments performed on multiple neural networks and the Microsoft Common Objects in Contexts (MS-COCO) dataset demonstrate the effectiveness of our method.