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Novelty detection using level set methods with adaptive boundaries

Ding, X, Li, Y, Belatreche, A and Maguire, L 2013, Novelty detection using level set methods with adaptive boundaries , in: Institute of Electrical and Electronics Engineers (IEEE) International Conference on Systems, Man, and Cybernetics, 13-16 October 2013, Manchester.

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Abstract

This paper proposes a locally adaptive level set boundary description (LALSBD) method for novelty detection. The proposed method adjusts the non linear boundary directly in the input space and consists of a number of processes including level set function (LSF) construction, local boundary evolution and 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 novel events more accurately, especially for applications which demand very high classification accuracy for normal events.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Systems, Man, and Cybernetics (SMC), 2013 Institute of Electrical and Electronics Engineers (IEEE) International Conference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:58
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33106

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