A Metric Learning-based Image Recognition Method

Abstract

Image recognition technology is an important branch of artificial intelligence research, using the computer to process, analyze and understand the image, in order to identify different patterns of objects. Image recognition technology is currently used in a wide range of applications, such as face recognition, fingerprint recognition, terrain survey, license plate recognition, etc. However, due to the possible existence of multiple categories in images, the blurring of graphic boundaries affects the results of image recognition. In this paper, we propose a multi-category multi-task image recognition method based on deep metric learning (MMDML). Specially, we combine triplet loss function and softmax loss function to construct the loss function, and take ResNet-50 network training to fit the optimal loss function for image recognition. To demonstrate the effectiveness of the proposed method, our method is compared with the other two methods on three common image recognition datasets, namely ImageNet, PASCALVOC, and Caltech. And the experimental results show that our algorithm has the highest Rank-1 and mAP on three datasets.