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.
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