Sparse representation has shown an attractive performance in a number of applications.
However, the available sparse representation methods still suffer from some problems, and …

Thanks to the powerful feature representations obtained through deep convolutional neural
network (CNN), the performance of object detection has recently been substantially boosted …

Two popular representation learning paradigms are dictionary learning and deep learning.
While dictionary learning focuses on learning “basis” and “features” by matrix factorization …

Conventional extreme learning machines (ELMs) solve a Moore–Penrose generalized
inverse of hidden layer activated matrix and analytically determine the output weights to …

Recently, a multi-view dictionary learning (DL) technique has received much attention.
Although some multi-view DL methods have been presented, they suffer from the problem of …

Deep convolutional neural networks (CNNs) have shown their great success on image
classification. CNNs mainly consist of convolutional and pooling layers, both of which are …

Recovering low-rank and sparse subspaces jointly for enhanced robust representation and
classification is discussed. Technically, we first propose a transductive low-rank and sparse …

Human activity analysis in videos has increasingly attracted attention in computer vision
research with the massive number of videos now accessible online. Although many …

Automatic violence detection from video is a hot topic for many video surveillance
applications. However, there has been little success in developing an algorithm that can …

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with
applications to robust visual recognition. Specifically, the concept of an ideal representation …