Keypoint-based graph contrastive neural network for image classification

At present, deep learning is one of the mainstream methods for image classification. It focuses more on local features in the receptive field than the prior information of topological structure of the category. In this paper, We propose a Keypoint-based Discriminator Graph neural network (Key-D-Graph) for image binary classification method, which is a graph comparison network method based on key points. It explicitly introduces the topology prior structure when identifying image categories. The method contains two main steps. The first step is to build the graph representation of an image with the keypoints, that is, identifying possible key points of the target category in the image by a deep learning method, and then using the coordinates of the key points to generate the topological representation of the image. The second step is to build a graph contrastive network based on the image representation of key points, so as to estimate the structural difference between the graph to be identified and the object graph, realizing object discrimination. In this step, the topological prior structure information of the object is used to realize object recognition based on the global structure information of the image. Especially, the intermediate output results of Key-D-Graph are the key points of categories containing explicit semantic information, which facilitates analysis and debugging of the algorithm step by step in practical application. Contrast experiments show that the proposed method outperforms the mainstream methods both in efficiency and precision. And the mechanism and effectiveness of topological structure in classification are verified by the ablation experiments.