Rapid and proficient data retrieval is an essential component of modern astronomical research. In this paper, we address the challenge of retrieving astronomical image content by leveraging state-of-the-art deep learning techniques. We have designed a retrieval model, HybridVR, that integrates the capabilities of the deep learning models ResNet50 and VGG16 and have used it to extract key features of solar activity and solar environmental characteristics from observed images. This model enables efficient image matching and allows for content-based image retrieval (CBIR). Experimental results demonstrate that the model can achieve up to 98% similarity during CBIR while exhibiting adaptability and scalability. Our work has implications for astronomical research, data management, and education, and it can contribute to optimizing the utilization of astronomical image data. It also serves as a useful example of the application of deep learning technology in the field of astronomy.

中文翻译：

---

AstroSer：利用深度学习在海量太阳观测图像中进行基于内容的高效检索


快速、熟练的数据检索是现代天文学研究的重要组成部分。在本文中，我们利用最先进的深度学习技术解决了检索天文图像内容的挑战。我们设计了一个检索模型HybridVR，它集成了深度学习模型ResNet50和VGG16的功能，并用它从观测图像中提取太阳活动和太阳环境特征的关键特征。该模型可实现高效的图像匹配，并允许基于内容的图像检索（CBIR）。实验结果表明，该模型在 CBIR 过程中可以实现高达 98% 的相似度，同时表现出适应性和可扩展性。我们的工作对天文学研究、数据管理和教育具有影响，并且有助于优化天文图像数据的利用。这也成为深度学习技术在天文学领域应用的有益范例。