TY - JOUR
T1 - Low-rank modeling and its applications in image analysis
AU - Zhou, Xiaowei
AU - Yang, Can
AU - Zhao, Hongyu
AU - Yu, Weichuan
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Low-rank modeling generally refers to a class of methods that solves 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 attention to this topic. In this article, we review the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis. We first give an overview of the concept of low-rank modeling and the 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 article with some discussions.
AB - Low-rank modeling generally refers to a class of methods that solves 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 attention to this topic. In this article, we review the recent advances of low-rank modeling, the state-of-the-art algorithms, and the related applications in image analysis. We first give an overview of the concept of low-rank modeling and the 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 article with some discussions.
KW - Image analysis
KW - Low-rank modeling
KW - Matrix factorization
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=84924971479&partnerID=8YFLogxK
U2 - 10.1145/2674559
DO - 10.1145/2674559
M3 - Journal article
AN - SCOPUS:84924971479
SN - 0360-0300
VL - 47
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 2
M1 - 36
ER -