Image Retrieval based on Deep Learning

Abstract

With the rapid growth of image data, how to efficiently and accurately extract useful features from massive image data and perform fast image retrieval has become an important research direction. This study focuses on the design and training of deep learning-based image feature extraction networks to improve the robustness and generalization of image features by optimizing the network structure and loss function. In order to evaluate the performance of the system, this study also designs appropriate evaluation indicators and conducts corresponding experiments. Through experimental verification, the results show that these methods can effectively improve the performance of image feature extraction and image retrieval, and have broad potential in practical applications.