Image semantic segmentation with finer edges and complete parts from bounding box annotations

Abstract

Image semantic segmentation understands and parses the image scene from the pixel-level. We work on the weakly supervised image semantic segmentation method on bounding box annotations. Inspired by Roberts edge detection operator, we develop an edge gradient module, which includes the max-pooling layer and average-pooling layer to extract edge gradient features. We combine the pyramid pooling (PP) module extracting global features with the atrous spatial PP module building the relationships between nodes to form long-range context dependency module. It can strengthen the ties between various parts of the object, just like a simplified fully connected conditional random field. To obtain more accurate feedback from bounding box annotations, we design a random label transformation algorithm. Finally, we demonstrate the validity of our module on PASCAL VOC 2012 and MS-COCO datasets, and our whole model has achieved a better performance than other mainstream methods.