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基于深度学习的通用本地图像检索系统设计
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Abstract:
随着大量数字图像数据的产生，高效准确的图像检索技术变得尤为重要。本文提出了一种结合深度学习和磁盘向量检索技术的通用本地图像检索系统，采用了深度神经网络模型作为特征提取的主要工具，通过深层网络结构捕获图像的高层语义信息，实现对图像内容的精细描述，旨在提升检索的准确性和效率，图像数据库的容量。由具体的实例数据验证说明了系统可用性，证明了其在实际应用中的广泛适用性，文中研究可对图像检索系统的进一步发展起到积极的参考作用。
With the generation of a massive amount of digital image data, efficient and accurate image retriev-al technology has become particularly important. This paper proposes a universal local image re-trieval system that combines deep learning and disk vector retrieval technology, utilizing deep neural network models as the main tool for feature extraction. By capturing the high-level semantic information of images through deep network structures, the system achieves a fine- grained de-scription of image content, aiming to enhance the accuracy and efficiency of retrieval, as well as the capacity of the image database. The usability of the system is demonstrated through specific instance data, proving its wide applicability in practical applications. The research presented in this paper can play a positive role in the further development of image retrieval systems.
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