2016/01/12 by Yuqing Hou, Hou, Yuqing, Zhouchen Lin +4
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Remote-Sensing Image Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1601.03055
4 pages
openalex publication_date 2016/01/12 · arxiv created 2016/06/21 · arxiv updated 2016/06/22 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.