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A locally adaptive boundary evolution algorithm for novelty detection using level set methods

Ding, X, Li, Y, Belatreche, A and Maguire, L 2014, A locally adaptive boundary evolution algorithm for novelty detection using level set methods , in: Institute of Electrical and Electronics Engineers (IEEE) 2014 International Joint Conference on Neural Networks (IJCNN), 6-11 July 2014, Beijing.

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Abstract

This paper proposes a new locally adaptive boundary evolution algorithm for level set methods (LSM) based novelty detection. The proposed approach consists of level set function construction, boundary evolution, and evolution termination. It utilises the exterior data points lying outside the decision boundary to effect the segments of the boundary that need to be locally evolved in order to make the boundary better fit the data distribution, so it can evolve boundary locally without requiring knowing explicitly the decision boundary. The experimental results demonstrate that the proposed approach can effectively detect novel events as compared to the reported LSM-based novelty detection method with global boundary evolution scheme and four representative novelty detection methods when there is an exacting error requirement on normal events.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Neural Networks (IJCNN), 2014 International Joint Conference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 19 Jun 2015 18:26
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33092

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