4/30/2023 0 Comments Xstand remote repository![]() Textual information is sometimes accompanied by additional encodings (such as visuals). In particular, we focus on the visual content-based image annotation techniques since they are one of the dynamic research fields nowadays. In this paper, we analyze various image annotation methods, namely: Visual Content-based and Users’ Tags-based Image Annotation Methods. Its policy consists in associating textual descriptions with the images, which constitute the focus of the research queries later on. 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. ![]() As an alternative, researchers’ efforts have been oriented towards the Text-based Image Retrieval (TBIR). 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. Typically, research studies in this area were focused on the Content-based Image Retrieval (CBIR). This has led to the emergence of several image retrieval studies during the past two decades. 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. In fact, images can be used in various real-world applications, like biology, medical diagnosis, space research, remote sensing, etc. 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.
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