基于子空间聚类的标签共享的复杂误差归纳标签矩阵精化
Subspace Clustering Based Tag Sharing for Inductive Tag Matrix Refinement with Complex Errors
摘要：
使用标签注释图像对于索引和检索图像很有用。然而，许多可用的注释数据包括缺失或不准确的注释。在本文中，我们提出了一个图像注释框架，依次执行标签完成和细化。我们利用数据的子空间属性，通过稀疏子空间聚类的标签完成。然后，我们提出了一种新的矩阵完成模型的标签细化，结合视觉相关性，语义相关性和复杂错误的新研究的性质。该方法在多个基准数据集上的性能优于最先进的方法，即使它们包含一定程度的注释噪声。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
