Image matching based on traditional algorithm and convolutional neural network
Liu Peiyang, Shi Xiujin · 2023
In recent years, with the progress of neural networks, people have gradually shifted their research focus from words to images, precisely because of the emergence of convolutional neural networks, which can convert the information of images into an easy-to-understand form and make it easier for people to solve problems in the image field. Image matching is the most fundamental research direction in the field of images. With the development of technology, the algorithms applied to image matching have gradually changed from traditional sift and orb algorithms to convolutional neural network-based algorithms, such as superPoint, superGlue, D2net and other models. Although these models have certain advantages over the traditional algorithms, there are still problems that the high-level features of the image are not closely related to the underlying features. To address these problems, this paper will use the traditional algorithm to fuse the features extracted by the convolutional neural network model to improve the matching accuracy, and combine the attention mechanism in the convolutional neural network to make better extraction of the easily ignored areas in the image. The proposed method is experimented on the Hpatches dataset and Brown dataset and achieved good results with an accuracy of 91.23% and 92.36%, respectively.