自动图像标注技术综述

图像自动标注技术是减少图像数据与内容之间“语义鸿沟”的其中一种最有效途径，对于帮助人类理解图像内容，从海量图像数据中检索感兴趣的信息具有重要现实意义.通过研究近20年公开发表的图像标注文献，总结了图像标注模型的一般性框架；并通过该框架结合各种具体工作，分析出在图像标注研究过程中需要解决的一般性问题；将各种图像标注模型所采用的主要方法归为9种类型，分别为相关模型、隐Markov模型、主题模型、矩阵分解模型、近邻模型、基于支持向量机的模型、图模型、典型相关分析模型以及深度学习模型，并对每种类型的图像标注模型，按照“基本原理介绍—具体模型差异—模型总结”3个层面进行了研究与分析.此外，总结了图像标注模型常用的一些数据集、评测指标，对一些比较著名的标注模型的性能进行了比较，并据此对各种类型的标注模型做了优缺点分析.最后，提出了图像标注领域一些开放式问题和研究方向.

Review of Automatic Image Annotation Technology

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.