  Recently many research efforts have been devoted to image annotation by
leveraging on the associated tags/keywords of web images as training labels. A
key issue to resolve is the relatively low accuracy of the tags. In this paper,
we propose a novel semi-automatic framework to construct a more accurate and
effective training set from these web media resources for each label that we
want to learn. Experiments conducted on a real-world dataset demonstrate that
the constructed training set can result in higher accuracy for image
annotation.
