Local linear transforms for texture measurements
Signal Processing
Self-organization and associative memory: 3rd edition
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The cascade-correlation learning architecture
Advances in neural information processing systems 2
Handwritten digit recognition with a back-propagation network
Advances in neural information processing systems 2
Floating search methods in feature selection
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Color image processing and applications
Color image processing and applications
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
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Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Network intrusion and fault detection: a statistical anomaly approach
IEEE Communications Magazine
Objective functions for training new hidden units in constructive neural networks
IEEE Transactions on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Dynamical behavior of autoassociative memory performing novelty filtering for signal enhancement
IEEE Transactions on Neural Networks
Detection of Unusual Objects and Temporal Patterns in EEG Video Recordings
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Combining SOM and local minimum enclosing spheres for novelty detection
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Rough-based semi-supervised outlier detection
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Semi-supervised outlier detection based on fuzzy rough C-means clustering
Mathematics and Computers in Simulation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Novelty detection in wildlife scenes through semantic context modelling
Pattern Recognition
FPGA implementation of a wavelet neural network with particle swarm optimization learning
Mathematical and Computer Modelling: An International Journal
Review: A review of novelty detection
Signal Processing
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This paper proposes a new model of "novelty detection” for image sequence analysis using neural networks. This model uses the concept of artificially generated negative data to form closed decision boundaries using a multilayer perceptron. The neural network output is novelty filtered by thresholding the output of multiple networks (one per known class) to which the sample is input and clustered for determining which clusters represent novel classes. After labeling these novel clusters, new networks are trained on this data. We perform experiments with video-based image sequence data containing a number of novel classes. The performance of the novelty filter is evaluated using two performance metrics and we compare our proposed model on the basis of these with five baseline novelty detectors. We also discuss the results of retraining each model after novelty detection. On the basis of Chi-square performance metric, we prove at 5 percent significance level that our optimized novelty detector performs at the same level as an ideal novelty detector that does not make any mistakes.