Abstract
Image Annotation (IA) followed by Image Retrieval (IR) plays a significant role in today’s computer vision world. As the manual IA is a tedious and time-consuming process, the automated IA became very predominant in the computer vision applications. IA deals with the assigning of meaningful labels to various objects in the image. The objective of this article is to represent the various IA approaches adopted in the last decade. Observation of the existing IA methods and their performances leads to identify the pitfalls the existing approaches. Few approaches used standard datasets and images downloaded from internet to evaluate the performance of the Image Annotation.
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