Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset
Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …
moving objects. Recent research on problem formulations based on decomposition into low …
Multi-label active learning algorithms for image classification: Overview and future promise
Image classification is a key task in image understanding, and multi-label image
classification has become a popular topic in recent years. However, the success of multi …
classification has become a popular topic in recent years. However, the success of multi …
Weighted nuclear norm minimization and its applications to low level vision
As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …
(NNM) problem has been attracting significant research interest in recent years. The …
On the applications of robust PCA in image and video processing
Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse
matrices offers a powerful framework for a large variety of applications such as image …
matrices offers a powerful framework for a large variety of applications such as image …
Weighted Schatten -Norm Minimization for Image Denoising and Background Subtraction
Low rank matrix approximation (LRMA), which aims to recover the underlying low rank
matrix from its degraded observation, has a wide range of applications in computer vision …
matrix from its degraded observation, has a wide range of applications in computer vision …
Weighted nuclear norm minimization with application to image denoising
As a convex relaxation of the low rank matrix factorization problem, the nuclear norm
minimization has been attracting significant research interest in recent years. The standard …
minimization has been attracting significant research interest in recent years. The standard …
Partial sum minimization of singular values in robust PCA: Algorithm and applications
Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for
recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers …
recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers …
Bayesian robust tensor factorization for incomplete multiway data
We propose a generative model for robust tensor factorization in the presence of both
missing data and outliers. The objective is to explicitly infer the underlying low …
missing data and outliers. The objective is to explicitly infer the underlying low …
Robust principal component analysis with complex noise
The research on robust principal component analysis (RPCA) has been attracting much
attention recently. The original RPCA model assumes sparse noise, and use the L_1-norm …
attention recently. The original RPCA model assumes sparse noise, and use the L_1-norm …
Denoising hyperspectral image with non-iid noise structure
Hyperspectral image (HSI) denoising has been attracting much research attention in remote
sensing area due to its importance in improving the HSI qualities. The existing HSI …
sensing area due to its importance in improving the HSI qualities. The existing HSI …