IEEE Trans Pattern Anal Mach Intell. 2017 Aug;39(8):1662-1674. doi: 10.1109/TPAMI.2016.2608882. Epub 2016 Sep 13.
Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
社交图像标签细化旨在通过自动完成缺失的标签和纠正噪声污染的标签来提高标签质量，是社交图像搜索的重要组成部分。传统方法主要侧重于探索视觉和标签信息，而不考虑用户信息，而用户信息通常揭示了社交图像的（不）正确标签的重要线索。为此，我们提出了一种新颖的三聚类张量完成框架，以协同探索这三种信息，从而提高社交图像标签细化的性能。具体来说，用户、图像和标签之间的相互关系通过张量来建模，而用户、图像和标签之间的内部关系则通过三个正则化项分别进行探索。为了解决需要昂贵计算和内存成本的超稀疏和大规模张量分解的挑战，我们提出了一种新的三聚类方法，通过同时将用户、图像和标签聚类成一定数量的三聚类，将张量划分为若干个子张量。然后，我们研究了两种策略来完成这些子张量，同时考虑子张量之间的（不）相关性。在真实的社交图像数据库上的实验结果表明，与最先进的方法相比，所提出的方法具有优越性。
