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

Xuemei Ding, Yuhua Li, Ammar Belatreche, Liam Maguire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1870-1876
Number of pages7
ISBN (Print)978-1-4799-6627-1
DOIs
Publication statusPublished - Jul 2014
EventIEEE 2014 International Joint Conference on Neural Networks (IJCNN) - Beijing, China
Duration: 1 Jul 2014 → …

Conference

ConferenceIEEE 2014 International Joint Conference on Neural Networks (IJCNN)
Period1/07/14 → …

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