Ulster University Logo

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

Ding, Xuemei, Li, Yuhua, Belatreche, Ammar and Maguire, Liam (2014) A locally adaptive boundary evolution algorithm for novelty detection using level set methods. In: IEEE 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China. IEEE. 7 pp. [Conference contribution]

Full text not available from this repository.

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889399&isnumber=6889358

DOI: 10.1109/IJCNN.2014.6889399

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 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:30303
Deposited By: Dr Ammar Belatreche
Deposited On:01 Oct 2014 10:11
Last Modified:01 Oct 2014 10:11

Repository Staff Only: item control page