# Low-Rank Modeling and Its Applications in Image Analysis

arXiv: https://arxiv.org/abs/1401.3409 · HTML: https://mlsanity.com/paper/1401.3409/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 15 of 15)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Matrix Compression via Randomized Low Rank and Low Precision Factorization](https://mlsanity.com/paper/2310.11028/) (2023)
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- [Segment Anything for Videos: A Systematic Survey](https://mlsanity.com/paper/2408.08315/) (2024)
- [Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation](https://mlsanity.com/paper/2102.12056/) (2021)
- [Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives](https://mlsanity.com/paper/2505.15222/) (2025)
- [Accurate and fast matrix factorization for low-rank learning](https://mlsanity.com/paper/2104.10785/) (2021)
- [MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information](https://mlsanity.com/paper/2303.02566/) (2023)
- [A method of using RSVD in residual calculation of LowBit GEMM](https://mlsanity.com/paper/2409.18772/) (2024)
- [Data-Adaptive Transformed Bilateral Tensor Low-Rank Representation for Clustering](https://mlsanity.com/paper/2510.20077/) (2025)
- [Fast and Effective Computation of Generalized Symmetric Matrix Factorization](https://mlsanity.com/paper/2603.19147/) (2026)
- [CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection](https://mlsanity.com/paper/2607.01870/) (2026)
- [CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection](https://mlsanity.com/paper/2608.28070/) (2026)

## Abstract (arXiv)

Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data mining, signal processing and bioinformatics. Recently, much progress has been made in theories, algorithms and applications of low-rank modeling, such as exact low-rank matrix recovery via convex programming and matrix completion applied to collaborative filtering. These advances have brought more and more attentions to this topic. In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis. We first give an overview to the concept of low-rank modeling and challenging problems in this area. Then, we summarize the models and algorithms for low-rank matrix recovery and illustrate their advantages and limitations with numerical experiments. Next, we introduce a few applications of low-rank modeling in the context of image analysis. Finally, we conclude this paper with some discussions.