Progressive Dynamic Graph Network for Image Segmentation

Objectives: Image segmentation is one of the important subjects in the field of image processing and computer vision. Image segmentation has a wide range of applications in robot perception, autonomous driving, medical image analysis, scene understanding, video surveillance, virtual reality and augmented reality, but it also faces many challenges. For example, it is necessary to rely on the context to obtain more precise results in complex scenes. Deep learning-based image segmentation algorithms have made great progress, but they also face many problems, especially most algorithms lack the ability of combining image context, and they ignore the role and influence of the contextual information on the segmentation contour, which limits the improvement of algorithm performance. For this reason, this paper proposes a contour-based image segmentation approach, which uses a progressive dynamic graph network to deform the contour. Methods: Specifically, according to the topology of object contour, vertices are sampled on the contour to convert it into a dynamic graph. This paper abandons the disorder of the graph and the uncertainty of the adjacency relationship to construct a graph with a fixed topology. And then this paper performs inference learning by diffusing contextual information of the object points. It also progressively accumulates historical learning experience to update the contour graph dynamically. This paper adopts an end-to-end method to update the graph and predict the object position, and encapsulates them into a closed-loop learning process. The approach in this paper fully takes into account context and the historical learning experience. It also makes up for the shortcoming of the pixel-wise segmentation methods and traditional ac-tive contour models. Results: Experiments on the Cityscapes, KINS, SBD datasets show that the effectiveness and speed of the proposed approach in real-time instance segmentation. Specifically, the dynamic graph model achieves the best performances on Cityscapes and SBD datasets, which are 34.4% AP and 55.3% AP respectively, and 30.5% AP on KINS dataset. It also achieves a better fitting effect on KINS dataset in terms of visualization results. Compared with Deep Snake model, segmentation contours which are extracted by dynamic graph model fit the real boundary better and its contours are smoother. In addition, in terms of running speed, the dynamic graph model has also obtained best results on three datasets, which are 3.8 FPS, 7.6 FPS and 13.1 FPS respectively. The proposed method has an average improvement of 0.5 FPS on three datasets which is compared with Deep Snake model. Conclusions: Progressive dynamic graph makes better use of the topology of object contour and the characteristics of dynamic update, and by disseminating context information and historical information, this method solves the problem that segmentation algorithms lack the ability of combining context. Future research can also face the following aspects: designing a more efficient and robust network architecture; considering the objects around the target to combine the context information of scene in the image; attempting to resolve the issue of poor convergence on non-convex objects or concave regions.