跨域图像检索综述

doi:10.3778/j.issn.1002-8331.2203-0312

摘要/Abstract

摘要： 受成像载体、成像光谱和成像条件等的影响，跨域图像在不同领域的应用日益增多，跨域图像检索已成为了许多领域研究的热点和前言。然而图像的跨域检索面临着图像视觉偏差的问题，通过传统同域图像检索的方法无法有效地得到结果。通过文献调研，系统梳理了近年来跨域图像检索领域的代表性方法。对跨域图像检索任务作出了简要说明并指出了关键问题；根据图像域的不同转换阶段，将跨域图像检索方法分为两类：基于特征空间迁移和基于图像域迁移的跨域图像检索方法，并对两类方法进行了系统总结和分析；整理了跨域图像检索在不同领域的数据集，对比了各类方法的性能；总结了现有跨域检索方法并对未来的研究方向进行了展望。

关键词: 跨域图像检索, 深度学习, 特征空间迁移, 图像域迁移

Abstract: Due to the influence of imaging carriers, imaging spectra and imaging conditions, the application of cross-domain images in different fields is increasing, and cross-domain image retrieval has become a hot topic and a preamble of research in many fields. However, cross-domain image retrieval faces the problem of image visual deviation, and the results cannot be effectively obtained through traditional same-domain image retrieval methods. Through literature research, systematically reviews the representative approaches in the field of cross-domain image retrieval in recent years. The task of cross-domain image retrieval is described and the key issues are pointed out. Cross-domain image retrieval methods are divided into two categories according to different transformation stages of image domains：cross-domain image retrieval methods based on feature space migration and cross-domain image retrieval methods based on image domain adaptation, and the two types of methods are summarised and analyzed systematically, and then, the datasets of cross-domain image retrieval in different fields are collated, and the performance of various methods is compared. The existing cross-domain retrieval methods are summarised and the future research directions are outlined.

Key words: cross-domain image retrieval, deep learning, feature space migration, image domain adaptation

李浩然, 周小平, 王佳. 跨域图像检索综述[J]. 计算机工程与应用, 2022, 58(15): 18-36.

LI Haoran, ZHOU Xiaoping, WANG Jia. Review of Cross-Domain Image Retrieval[J]. Computer Engineering and Applications, 2022, 58(15): 18-36.

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