# Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging

arXiv: https://arxiv.org/abs/1804.04397 · HTML: https://mlsanity.com/paper/1804.04397/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Tensor Networks Meet Neural Networks: A Survey and Future Perspectives](https://mlsanity.com/paper/2302.09019/) (2023)
- [Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition](https://mlsanity.com/paper/2112.10992/) (2021)

## Abstract (arXiv)

Image retagging aims to improve tag quality of social images by refining their original tags or assigning new high-quality tags. Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by constructing and exploring an image-tag-user graph. However, such methods will become computationally infeasible with the rapidly increasing number of images, tags and users. It has been proven that Anchor Graph Regularization (AGR) can significantly accelerate large-scale graph learning model 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 effectively refine the tags of social images, which is insensitive to the scale of the applied 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 SUGAR is implemented on the original image-tag-user tensor to refine the tags of the anchor images. Third, we efficiently assign tags to non-anchor images by leveraging the relationship between the non-anchor images and the anchor units. Experimental results on a real-world social image database well demonstrate the effectiveness of SUGAR-TC, outperforming several related methods.