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A review on visual content-based and users' tags-based image annotation: methods and techniques

机译：基于Visual Content和用户标签的图像注释述评：方法和技术

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In the current era of digital communication, the use of images is growing exponentially since they are one of the best ways of expressing, sharing and memorizing knowledge. In fact, images can be used in various real-world applications, like biology, medical diagnosis, space research, remote sensing, etc. However, finding the most relevant images that meet the users' needs is a challenging task, especially when the search is performed over gigantic amounts of images. This has led to the emergence of several image retrieval studies during the past two decades. Typically, research studies in this area were focused on the Content-based Image Retrieval (CBIR). However, extensive research have proved that there is a 'semantic gap' between the visual information captured by the imaging devices and the image semantics understandable by humans. As an alternative, researchers' efforts have been oriented towards the Text-based Image Retrieval (TBIR). Indeed, TBIR is a typical method that helps bridge the issue of 'semantic gap' between the low-level image features and the high-level image semantics. Its policy consists in associating textual descriptions with the images, which constitute the focus of the research queries later on. In this paper, we analyze various image annotation methods, namely: Visual Content-based and Users' Tags-based Image Annotation Methods. In particular, we focus on the visual content-based image annotation techniques since they are one of the dynamic research fields nowadays.
机译：在当前的数字通信时代，图像的使用是指数增长的，因为它们是表达，共享和记忆知识的最佳方式之一。事实上，图像可以在各种现实应用程序中使用，如生物学，医学诊断，空间研究，遥感等，但是找到满足用户需求的最相关的图像是一个具有挑战性的任务，尤其是在搜索时在巨大的图像上进行。这导致了过去二十年中几个图像检索研究的出现。通常，该区域的研究研究专注于基于内容的图像检索（CBIR）。然而，已经证明了广泛的研究，即由成像装置捕获的视觉信息与人类可以理解的图像语义之间存在“语义差距”。作为替代方案，研究人员已经朝向基于文本的图像检索（TBIR）的努力。实际上，TBIR是一种典型的方法，有助于弥合低级图像特征和高级图像语义之间的“语义差距”问题。其策略包括将文本描述与图像相关联，该图像稍后构成研究查询的重点。在本文中，我们分析了各种图像注释方法，即：基于视觉内容和用户的基于标签的图像注释方法。特别是，我们专注于基于视觉内容的图像注释技术，因为它们是如今的动态研究领域之一。

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