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| Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsupervised Expectation Maximization Classifier for Imaging Surveillance Application
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Full
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Source |
International Journal of Image Processing (IJIP) |
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Table of Contents |
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Volume: 2 Issue: 1 |
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Pages: 1-34 |
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Publication
Date: February 2008 |
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ISSN
(Online): 1985-2304 |
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Pages |
18 - 26 |
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Author(s) |
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Published
Date |
30-02-2008 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Consistency Subset Evaluator, Principal Component Analysis, Unsupervised Expectation Maximization, Classification, Imaging surveillance |
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| This paper presents the application of multi dimensional feature reduction of
Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA)
and Unsupervised Expectation Maximization (UEM) classifier for imaging
surveillance system. Recently, research in image processing has raised much
interest in the security surveillance systems community. Weapon detection is one
of the greatest challenges facing by the community recently. In order to
overcome this issue, application of the UEM classifier is performed to focus on
the need of detecting dangerous weapons. However, CSE and PCA are used to
explore the usefulness of each feature and reduce the multi dimensional features
to simplified features with no underlying hidden structure. In this paper, we take
advantage of the simplified features and classifier to categorize images object
with the hope to detect dangerous weapons effectively. In order to validate the
effectiveness of the UEM classifier, several classifiers are used to compare the
overall accuracy of the system with the compliment from the features reduction of
CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased
Clustering and k-Means methods. The final outcome of this research
clearly indicates that UEM has the ability in improving the classification accuracy
using the extracted features from the multi-dimensional feature reduction of CSE.
Besides, it is also shown that PCA is able to speed-up the computational time
with the reduced dimensionality of the features compromising the slight decrease
of accuracy. |
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| Chue-Poh Tan : Colleagues
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| Ka-Sing Lim : Colleagues
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| Weng-Kin Lai : Colleagues
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