ISSN 1000-1239 CN 11-1777/TP

• 图形图像 •

### 自动图像标注技术综述

1. (武汉理工大学计算机科学与技术学院 武汉 430070) (mayanchun@whut.edu.cn)
• 出版日期: 2020-11-01
• 基金资助:
国家自然科学基金项目(61602353)；中央高校基本科研业务费专项资金(WUT:2017YB028)

### Review of Automatic Image Annotation Technology

Ma Yanchun, Liu Yongjian, Xie Qing, Xiong Shengwu, Tang Lingli

1. (School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070)
• Online: 2020-11-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61602353) and the Fundamental Research Funds for the Central Universities (WUT:2017YB028).

Abstract: As one of the most effective ways to reduce the “semantic gap” between image data and its content, automatic image annotation (AIA) technology has shown its great significance to help people understand image contents and retrieve the target information from the massive image data. This paper summarizes the general framework of AIA models by investigating the literatures about image annotation in recent 20 years, and analyzes the general problems to solve in AIA problems by combining the framework with various specific works. In this paper, the main methods used in various AIA models are classified into 9 types: correlation model, hidden Markov model, topic model, matrix factorization model, neighbor-based model, SVM-based model, graph-based model, CCA (KCCA) model and deep learning model. For each type of image annotation model, this paper provides a detailed study and analysis in terms of “basic principle introduction-specific model differences-model summary”. In addition, this paper summarizes some commonly used datasets and evaluation indexes, and compares the performance of some important image annotation models with related analysis on the advantages and disadvantages of various types of AIA models. Finally, some open problems and research directions in the field of image annotation are proposed and suggested.