With the rapid development of information technology and the arrival of the era of big data, people’s access to information is increasingly relying on information such as images. Today, image data are showing an increasing trend in the form of an index. How to use deep learning models to extract valuable information from massive data is very important. In the face of such a situation, people cannot accurately and timely find out the information they need. Therefore, the research on image retrieval technology is very important. Image retrieval is an important technology in the field of computer vision image processing. It realizes fast and accurate query of similar images in image database. The excellent feature representation not only can represent the category information of the image but also capture the relevant semantic information of the image. If the neural network feature learning expression is combined with the image retrieval field, it will definitely improve the application of image retrieval technology. To solve the above problems, this article studies the problems encountered in deep learning neural network feature learning based on image self-encoding and discusses its feature expression in the field of image retrieval. By adding the spatial relationship information obtained by image self-encoding in the neural network training process, the feature expression ability of the selected neural network is improved, and the neural network feature learning based on image coding is successfully applied to the popular field of image retrieval.

中文翻译：

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基于图像自编码的神经网络特征学习

随着信息技术的飞速发展和大数据时代的到来，人们对信息的获取越来越依赖于图像等信息。如今，图像数据以指数的形式呈现增长趋势。如何使用深度学习模型从海量数据中提取有价值的信息非常重要。面对这样的情况，人们无法准确及时地找到自己需要的信息。因此，对图像检索技术的研究非常重要。图像检索是计算机视觉图像处理领域的一项重要技术。实现对图像数据库中相似图像的快速准确查询。优秀的特征表示不仅可以表示图像的类别信息，还可以捕捉图像的相关语义信息。如果将神经网络特征学习表达与图像检索领域相结合，必将提升图像检索技术的应用。针对上述问题，本文研究了基于图像自编码的深度学习神经网络特征学习中遇到的问题，并探讨了其在图像检索领域中的特征表达。通过在神经网络训练过程中加入图像自编码得到的空间关系信息，提高所选神经网络的特征表达能力，