图像自动标注技术研究进展  

Research Progress on Automatic Image Annotation Technology

作　　者：杨诗曼 王中训[1,2] 吴文静 于乐凯 YANG Shiman;WANG Zhongxun;WU Wenjing;YU Lekai(School of Physics and Electronic Information,Yantai University,Yantai 264005,China;Shandong Data Open Innovation Application Laboratory of Smart grid Advanced Technology,Yantai University,Yantai 264005,China)

机构地区：[1]烟台大学物理与电子信息学院,山东烟台264005 [2]烟台大学智慧电网先进技术山东省数据开放创新应用实验室,山东烟台264005

出　　处：《探测与控制学报》2025年第1期24-32,40,共10页Journal of Detection & Control

基　　金：国防科技重点实验室基金项目(2021-JCJQ-LB-018)。

摘　　要：图像数据的快速增长以及对更智能、高效处理图像信息的迫切需求使得图像自动标注的需求日益增大,因此对图像自动标注方法研究进展进行总结。首先将图像自动标注的主体框架分为特征提取和标注模型两部分;其次对传统图像自动标注方法和深度学习的图像自动标注方法进行分析,从特征提取以及标注模型的角度展开研究;最后对图像自动标注发展中存在的问题进行分析总结,并对未来发展进行展望。The rapid growth of image data and the urgent demand for more intelligent and efficient processing of image information have led to a increasing growing need for automatic image annotation.Therefore,this paper summarized the research progress on methods for automatic image annotation.Firstly,the main framework of automatic image annotation was divided into two parts:feature extraction and annotation model.Then,both traditional methods and deep learning methods for automatic image annotation were analyzed,focusing on research from the perspectives of feature extraction and annotation models.Finally,this paper analyzed and summarized the problems in the development of automatic image annotation and provided an outlook on future developments.

关 键 词：自动标注 图像 特征提取 标注模型 深度学习 

分 类 号：TN957[电子电信—信号与信息处理]