Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Background model initialization is commonly the first step of the background subtraction
process. In practice, several challenges appear and perturb this process, such as dynamic …

In this paper, we consider the tensor completion problem, which aims to estimate missing
values from limited information. Our model is based on the recently proposed tensor-SVD …

We generalize the 1-bit matrix completion problem to higher order tensors. Consider a rank-r
order-d tensor T in ℝ N×⋯× ℝ N with bounded entries. We show that when r= O (1), such a …

Extracting the background from a video in the presence of various moving patterns is the
focus of several background-initialization approaches. To model the scene background …

Since automated cleaning systems are less common in extreme surveillance environments,
the accumulations of combustion fuels, dust, dirt, spider webs, etc., affect the visibility and …

Although image set classification has attracted great attention in computer vision and pattern
recognition communities, however, learning a compact and discriminative representation is …

Tensor completion and robust principal component analysis have been widely used in
machine learning while the key problem relies on the minimization of a tensor rank that is …

Classification based on image sets has recently attracted great interest in computer vision
community. In this paper, we proposed a transductive Tensor-driven Low-rank Discriminant …