TY - GEN

T1 - Greedy vs. L1 convex optimization in sparse coding

T2 - International Conference on Machine Learning 2015

AU - Ren, Huamin

AU - Pan, Hong

AU - Olsen, Søren Ingvor

AU - Moeslund, Thomas B.

N1 - Conference code: 31

PY - 2015

Y1 - 2015

N2 - Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes.During this procedure, sparse codes which can be achieved through finding the L0-norm solution of the problem: min ||Y -D_{alpfa}||–2^2 +||alpha||_0, is crucial. Note that D refers to the dictionary and refers to the sparse codes. This L0-norm solution, however, is known as a NP-hard problem. Despite of the research achievements in some classification fields, such as face and action recognition, a comparative study of codes in abnormal event detection is less studied and hence no conclusion is gained on the effect of codes in detecting abnormalities. We constrict our comparison in two types of the above L0-norm solutions: greedy algorithms and convex L1-norm solutions. Consideringthe property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classificationapplication may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate their performance from various aspects to better understand their applicability, including computationtime, reconstruction error, sparsity, detection

AB - Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes.During this procedure, sparse codes which can be achieved through finding the L0-norm solution of the problem: min ||Y -D_{alpfa}||–2^2 +||alpha||_0, is crucial. Note that D refers to the dictionary and refers to the sparse codes. This L0-norm solution, however, is known as a NP-hard problem. Despite of the research achievements in some classification fields, such as face and action recognition, a comparative study of codes in abnormal event detection is less studied and hence no conclusion is gained on the effect of codes in detecting abnormalities. We constrict our comparison in two types of the above L0-norm solutions: greedy algorithms and convex L1-norm solutions. Consideringthe property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classificationapplication may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate their performance from various aspects to better understand their applicability, including computationtime, reconstruction error, sparsity, detection

KW - Faculty of Science

KW - Machine learning

KW - Computer Vision

KW - Optimization

M3 - Conference article

VL - 37

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

SN - 1533-7928

Y2 - 1 June 2015

ER -