语义鸿沟的社会化：图像标签分配、细化和检索的比较研究
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
摘要：
在以前的评论基于内容的图像检索强调什么可以在图像中看到，以弥合语义差距，这项调查认为人们对图像的标签。一个全面的论述三个密切相关的问题，即，图像标签分配、细化和基于标签的图像检索。虽然现有的作品在其目标任务和方法方面有所不同，但它们依赖于标签相关性的关键功能，即估计特定标签与给定图像及其社会背景的视觉内容的相关性。通过分析一种特定的方法利用什么信息来构建其标签相关函数以及如何利用这些信息，本文介绍了一种分类法来构建不断增长的文献，了解主要作品的成分，澄清它们的联系和区别，并认识到它们的优点和局限性。为了进行最先进技术之间的头对头比较，提出了一种新的实验方案，训练集包含10k，100k和1m图像，并对三个测试集进行了评估，这些测试集由各个研究小组提供。对11个代表作品进行了实施和评价。将所有这些放在一起，调查的目的是概述过去，并促进在不久的将来取得进展。
英文摘要：
Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
