Sparse Modeling of Human Actions from Motion Imagery

Abstract

In treating Human Immunodeficiency Virus (HIV) infection, strict adherence to drug therapy is crucial in maintaining a low viral load, but the high dosages required for this often have toxic side effects which make perfect adherence to Antiretroviral Therapy (ART) unsustainable. The imperfect patient adherence to ART and the development of resistant strains in the viral load has led to the development of alternative treatments that incorporate immunological response. This paper investigates theoretically and numerically the effect of immune effectors, such as the cytotoxic lymphocyte (CTL), in modeling HIV pathogenesis; our results suggest the significant impact of the immune response on the control of the virus during primary infection. Qualitative aspects (including positivity, boundedness, stability, uncertainty, and sensitivity analysis) are addressed. Additionally, by introducing drug therapy, we analyze numerically the model to assess the effect of treatment consisting of a combination of several antiretroviral drugs. Nevertheless, even in the presence of drug therapy, ongoing viral replication can lead to the emergence of drug-resistant virus variances. This fact is addressed in our model by including two viral strains, wild-type and drug-resistant. Our results show that the inclusion of the CTL compartment produces a higher rebound for an individual's healthy helper T-cell compartment than does drug therapy alone. Furthermore, we quantitatively characterize successful drugs or drug combination scenarios for both strains of virus.

Towards Unsupervised Sudden Group Movement Discovery for Video Surveillance

“…Efros et al (Efros et al, 2003) aims at recognizing human action at a distance, using noisy optical flow. Other efficient similar techniques for action recognition in realistic videos can be cited (Gaidon et al, 2011;Castrodad and Sapiro, 2012). Kellokumpu et al (Kellokumpu et al, 2008) calculate local binary patterns along the temporal dimension and store a histogram of non-background responses in a spatial grid.…”

Learning a structure adaptive dictionary for sparse representation based classification

“…Yang et al [20] introduced Fisher discrimination both in the sparse coding coefficients and class-specific representations. Castrodad et al [21] learnt a set of actionspecific dictionaries with non-negative penalty on both dictionary atoms and representation coefficients. Instead of adding the sparsity constraint on the coefficients of each input signal, Meng et al [22] proposed to learn a dictionary under global sparsity constraint to fittingly assign the atoms of the dictionary to represent signals.…”

A Weighted Block Dictionary Learning Algorithm for Classification

“…In last decades, many famous dictionary learning approaches have been proposed. These approaches can be divided into two categories: unsupervised dictionary learning (UDL) approaches [2] and supervised dictionary learning (SDL) approaches [3][4][5][6][7]. UDL learns dictionary using unlabeled training samples.…”

“…Depending on whether training samples have been labeled, current dictionary learning approaches can be divided into two types: UDL approach and SDL approach. However, depending on whether atoms have been labeled, current dictionary learning approaches also can be divided into two main types: shared dictionary learning approaches 2 Mathematical Problems in Engineering [1,[19][20][21][22][23] and class-specific dictionary learning approaches [3][4][5][6][7]. In shared dictionary learning approaches, all atoms do not have label information and are shared by samples from all classes.…”

“…The shared dictionary [1, 19-23] could be regarded as a special case of our WBDL model when weight matrix is an all-one matrix. The class-specific dictionary [3][4][5][6][7] also could be regarded as a special case of our WBDL model when weight vector has only one unique nonzero element 1. Compared to shared dictionary and class-specific dictionary, our proposed model is more flexible, and it increases discriminability and reduces redundancy simultaneously.…”