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Novelty Detection Using Level Set Methods with Adaptive Boundaries

Ding, Xuemei, Li, Yuhua, Belatreche, Ammar and Maguire, Liam (2013) Novelty Detection Using Level Set Methods with Adaptive Boundaries. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester. IEEE. 6 pp. [Conference contribution]

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DOI: 10.1109/SMC.2013.515


This paper proposes a locally adaptive level setboundary description (LALSBD) method for novelty detection.The proposed method adjusts the nonlinear boundary directly inthe input space and consists of a number of processes includinglevel set function (LSF) construction, local boundary evolutionand termination. It employs kernel density estimation (KDE) to construct the LSF and form the initial boundary surrounding the training data. In order to make the boundary better fit the data distribution, a data-driven based local expanding/shrinking evolution method is proposed instead of the global evolution approach reported in our previous level set boundary description (LSBD) method. The proposed LALSBD is compared with LSBD and other four representative novelty detection methods. The experimental results demonstrate that LALSBD can detect novelevents more accurately, especially for applications which demand very high classification accuracy for normal events.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:28186
Deposited By: Dr Ammar Belatreche
Deposited On:14 Feb 2014 12:15
Last Modified:14 Feb 2014 12:15

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