Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
复制标题
用于大规模图像重新标记的社交锚单元图正则化张量补全
DOI：
10.1109/tpami.2019.2906603
复制
发表时间：
2019-08-01
影响因子：
23.6
通讯作者：
Tian, Qi
中科院分区：
文献类型：
--
作者：
Tang, Jinhui;Shu, Xiangbo;Tian, Qi
图像重标记的目的是通过补齐缺失的标签、纠正受噪声污染的标签以及分配新的高质量标签来提高社交图像的标签质量。最近的方法同时探索视觉、用户和标签信息，通过挖掘标签-图像-用户关联来提高图像重新标注的性能。然而，随着图像、标签和用户数量的迅速增加，这样的方法在计算上将变得不可行。已有研究证明，锚图只需探索少量的锚点，就能显著加速大规模的基于图的学习。受此启发，我们提出了一种新的社会锚-单元图正则化张量补全方法(SCOGE-TC)，该方法可以有效地精化社会图像的标签，而不受数据规模的影响。首先，我们构建了一个跨多个域(例如，图像域和用户域)的锚单元图，而不是单个域中的传统锚单元图。其次，提出了一种基于社会锚点单元图正则化的张量补全方法来细化锚点图像的标签。最后，我们通过利用非锚定单元和锚定单元之间的关系来有效地为非锚定图像分配标签。在真实社会图像数据库上的实验结果很好地证明了该算法的有效性和高效性，优于目前最先进的方法。
Image retagging aims to improve the tag quality of social images by completing the missing tags, rectifying the noise-corrupted tags, and assigning new high-quality tags. Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by mining the tag-image-user associations. However, such methods will become computationally infeasible with the rapidly increasing number of images, tags and users. It has been proven that the anchor graph can significantly accelerate large-scale graph-based learning by exploring only a small number of anchor points. Inspired by this, we propose a novel Social anchor-Unit GrAph Regularized Tensor Completion (SUGAR-TC) method to efficiently refine the tags of social images, which is insensitive to the scale of data. First, we construct an anchor-unit graph across multiple domains (e.g., image and user domains) rather than traditional anchor graph in a single domain. Second, a tensor completion based on Social anchor-Unit GrAph Regularization (SUGAR) is implemented to refine the tags of the anchor images. Finally, we efficiently assign tags to non-anchor images by leveraging the relationship between the non-anchor units and the anchor units. Experimental results on a real-world social image database well demonstrate the effectiveness and efficiency of SUGAR-TC, outperforming the state-of-the-art methods.