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In order to evaluate automated image annotation and object recognition algorithms, ground truth in the form of a set of images correctly annotated with text describing each image is required. In this paper, three image annotation approaches are reviewed: free text annotation, keyword annotation and annotation based on ontologies. The practical aspects of image annotation are then considered. We discuss the creation of keyword vocabularies for use in automated image annotation evaluation. As direct manual annotation of images requires much time and effort, we also review various methods to make the creation of ground truth more efficient. An overview of annotated image datasets for computer vision research is provided.