Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion
This work addresses tag quality issues in image retrieval systems, which is an incremental improvement for users relying on crowdsourced tags.
The paper tackled the problem of deficient and inaccurate tags in tag-based image retrieval by proposing a subspace clustering and matrix completion algorithm, which outperformed state-of-the-art approaches on benchmark datasets for image annotation.
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.