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Greedy vs. L1 convex optimization in sparse coding: comparative study in abnormal event detection

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Dokumenter

Huamin Ren, Hong Pan, Søren Ingvor Olsen, Thomas B. Moeslund

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. Considering
the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification
application 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 computation
time, reconstruction error, sparsity, detection
OriginalsprogEngelsk
TidsskriftJournal of Machine Learning Research
Vol/bind37
Antal sider6
ISSN1533-7928
StatusUdgivet - 2015
BegivenhedInternational Conference on Machine Learning 2015 - Lille, Frankrig
Varighed: 1 jun. 2015 → …
Konferencens nummer: 31

Konference

KonferenceInternational Conference on Machine Learning 2015
Nummer31
LandFrankrig
ByLille
Periode01/06/2015 → …

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