Research on image features extraction based on graph convolutional network (Withdrawal Notice)
Yingyun Zheng · 2023
With the continuous development and progress of information technology and image sensors, the amount of data contained in digital images is increased. Therefore, the image feature extraction and downstream applications have begun to receive widespread attention by computer vision field. However, the majority of existing contributions are concentrated on the utilization of machine learning models and ignore the graph convolution theory can also be utilized to extract the small differences features from input images. In this paper, we utilize the graph convolution model to dispose the issue that small differences between classes make sample differentiation difficult in the process of feature extraction with optimization loss functions in the feature space. From our experimental results, we can conclude that our proposed model can achieve the image feature extraction and validate the identification accuracy reaching to approximately 95% for image classification task with reasonable system costs.