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

Abstract

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.