Research on the construction of image similarity calculation model based on convolutional neural network

With the update iteration of deep learning technology, the necessity of image similarity calculation in image retrieval, target detection and tracking has become more and more prominent, and it has become an important force to promote the development of computer vision and artificial intelligence. In the face of increasingly complex and diverse application scenarios, the traditional image similarity detection technology has obvious shortcomings in calculation accuracy, and the image feature extraction needs manual assistance in the calculation process, so that the overall robustness of the technology is poor and cannot meet the practical application requirements. In view of this, this paper integrates the powerful image feature extraction ability of convolutional neural network, and constructs an image similarity calculation model based on VGG16- Siamese neural network, aiming at improving the accuracy of image similarity calculation and providing data support for the next technical application. Practice has proved that the VGG16 part of the model is used as the backbone feature extraction network to obtain the feature vector of the image. The Siamese neural network can use the feature vectors of two images to construct the distance similarity function at the same time, so as to calculate the similarity of the two images. The final experimental results show that the image similarity calculation model of VGG16- Siamese neural network has a precision of 90.84% and a recall rate of 91.88%, which meets the design expectation and has certain application and promotion value.