  Image annotation for active learning is labor-intensive. Various automatic
and semi-automatic labeling methods are proposed to save the labeling cost, but
a reduction in the number of labeled instances does not guarantee a reduction
in cost because the queries that are most valuable to the learner may be the
most difficult or ambiguous cases, and therefore the most expensive for an
oracle to label accurately. In this paper, we try to solve this problem by
using image metadata to offer the oracle more clues about the image during
annotation process. We propose a Context Aware Image Annotation Framework
(CAIAF) that uses image metadata as similarity metric to cluster images into
groups for annotation. We also present useful metadata information as context
for each image on the annotation interface. Experiments show that it reduces
that annotation cost with CAIAF compared to the conventional framework, while
maintaining a high classification performance.
