Active tagging for image indexing
Concept labeling and ontology-free tagging are the two typical manners of image annotation. Despite extensive research efforts have been dedicated to labeling, currently automatic image labeling algorithms are still far from satisfactory, and meanwhile manual labeling is rather labor-intensive. In contrast with labeling, tagging works in a free way and therefore it has better user experience for annotators. In this paper, we introduce an active tagging scheme that combines human and computer to assign tags to images. The scheme works in an iterative way. In each round, the most informative images are selected for manual tagging, and the remained images can be annotated by a tag prediction component. We have integrated multiple criteria for sample selection, including ambiguity, citation, and diversity. Experiments are conducted on different datasets and empirical results have demonstrated the effectiveness of the proposed approach.