Automatic image annotation is a crucial area in computer vision, which plays a significant role in image retrieval, image description, and so on. Along with the internet technique developing, there are numerous images posted on the web, resulting in the fact that it is a challenge to annotate images only by humans. Hence, many computer vision researchers are interested in automatic image annotation and make a great effort in optimizing its performance. Automatic image annotation is a task that assigns several tags in a limited vocabulary to describe an image. There are many algorithms proposed to tackle this problem and all achieve great performance. In this paper, we review seven algorithms for automatic image annotation and evaluate these algorithms leveraging different image features, such as color histograms and Gist descriptor. Our goal is to provide insights into the automatic image annotation. A lot of comprehensive experiments, which are based on Corel5K, IAPR TC-12, and ESP Game datasets, are designed to compare the performance of these algorithms. We also compare the performance of traditional algorithms employing deep learning features. Considering that not all associated labels are annotated by human annotators, we leverage the DIA metrics on IAPR TC-12 and ESP Game datasets.

自动图像批注是计算机视觉中的关键领域，在图像检索，图像描述等方面起着重要作用。随着互联网技术的发展，网络上发布了大量图像，这导致仅由人类注释图像是一个挑战。因此，许多计算机视觉研究人员对自动图像注释感兴趣，并在优化其性能方面付出了巨大的努力。自动图像注释是一项任务，该任务在有限的词汇表中分配多个标签来描述图像。提出了许多算法来解决此问题，并且所有算法均具有出色的性能。在本文中，我们回顾了7种用于自动图像标注的算法，并利用不同的图像特征（例如颜色直方图和Gist描述符）对这些算法进行了评估。我们的目标是深入了解自动图像注释。设计了许多基于Corel5K，IAPR TC-12和ESP Game数据集的综合实验来比较这些算法的性能。我们还比较了采用深度学习功能的传统算法的性能。考虑到并非所有相关标签都由人工注释者注释，因此我们在IAPR TC-12和ESP Game数据集上利用DIA指标。