Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
This addresses the issue of unreliable image tagging for indexing and retrieval, though it appears incremental as it builds on existing subspace clustering and matrix completion methods.
The paper tackles the problem of incomplete and inaccurate image annotations by proposing a sequential framework for tag completion and refinement, achieving state-of-the-art performance on multiple benchmark datasets with annotation noise.
Annotating images with tags is useful for indexing and retrieving images. However, many available annotation data include missing or inaccurate annotations. In this paper, we propose an image annotation framework which sequentially performs tag completion and refinement. We utilize the subspace property of data via sparse subspace clustering for tag completion. Then we propose a novel matrix completion model for tag refinement, integrating visual correlation, semantic correlation and the novelly studied property of complex errors. The proposed method outperforms the state-of-the-art approaches on multiple benchmark datasets even when they contain certain levels of annotation noise.