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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Markou, M. Singh, S. |
| Copyright Year | 1979 |
| Abstract | 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 |
| Sponsorship | IEEE Computer Society |
| Page Count | 14 |
| File Size | 4544677 |
| Starting Page | 1664 |
| Ending Page | 1677 |
| File Format | |
| ISSN | 01628828 |
| Volume Number | 28 |
| Issue Number | 10 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Detectors Image sequence analysis Learning systems System testing Artificial neural networks Information filtering Information filters Labeling Measurement feature extraction and selection. Novelty detection neural networks video analysis object classification |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software |
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