计算机技术与发展2026，Vol.36Issue(2)：16-21,6.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0226
面向图像检索的混合学习索引方法
A Blended Learned Index Approach for Image Retrieval
摘要
Abstract
In order to solve the problem of semantic gap and dimensional disaster in the processing of large-scale high-dimensional data by traditional image retrieval methods,we propose a Multimodal Dynamic Learned Index(MDLI).The method achieves a breakthrough through a three-level synergy mechanism.Firstly,the hierarchical adaptive weighting module is designed to achieve multi-scale feature fusion,and the local details and global semantics of different levels of ResNet are integrated.Secondly,the improved Graph Attention Network(GATv2)is introduced to dynamically model the complex relationship between images,and the top-20 edge sparsity strategy is combined to improve the computational efficiency.Finally,a hybrid index architecture is constructed,which organically combines the learned MLP index with the traditional VP-Tree,and optimizes the retrieval performance through the dynamic routing mechanism.Ex-periments on MNIST,CIFAR-10 and ImageNet-1K datasets show that the proposed method is significantly better than the existing methods in terms of retrieval accuracy and efficiency,and provides a solution for large-scale image retrieval that takes into account both accuracy and efficiency.关键词
学习索引/图像检索/图神经网络/混合索引/多尺度特征融合Key words
learned index/image retrieval/graph neural networks/mixed indexes/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
彭永鑫..面向图像检索的混合学习索引方法[J].计算机技术与发展,2026,36(2):16-21,6.基金项目
2024年陕西省教育科学研究计划项目(24JK0421) （24JK0421）
2021年商洛学院自然科学项目(21SKY004) （21SKY004）
