  Tag-based image retrieval (TBIR) has drawn much attention in recent years due
to the explosive amount of digital images and crowdsourcing tags. However, TBIR
is still suffering from the incomplete and inaccurate tags provided by users,
posing a great challenge for tag-based image management applications. In this
work, we proposed a novel method for image annotation, incorporating several
priors: Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors.
Highly representative CNN feature vectors are adopt to model the tag-visual
correlation and narrow the semantic gap. And we extract word vectors for tags
to measure similarity between tags in the semantic level, which is more
accurate than traditional frequency-based or graph-based methods. We utilize
the accelerated proximal gradient (APG) method to solve our model efficiently.
Extensive experiments conducted on multiple benchmark datasets demonstrate the
effectiveness and robustness of the proposed method.
