  Tag-based image retrieval (TBIR) has drawn much attention in recent years due
to the explosive amount of digital images and crowdsourcing tags. However, the
TBIR applications still suffer from the deficient and inaccurate tags provided
by users. Inspired by the subspace clustering methods, we formulate the tag
completion problem in a subspace clustering model which assumes that images are
sampled from subspaces, and complete the tags using the state-of-the-art Low
Rank Representation (LRR) method. And we propose a matrix completion algorithm
to further refine the tags. Our empirical results on multiple benchmark
datasets for image annotation show that the proposed algorithm outperforms
state-of-the-art approaches when handling missing and noisy tags.
