Name
Abstract | ||
|---|---|---|
When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is then used to detect the most informative samples to be labeled by a user through active learning techniques. By selecting unlabeled samples in a smart way and by adopting a diversity criterion, it is possible to obtain an accurate description of the class of interest with a relatively small number of training samples. The performance of the proposed method is illustrated in a change detection scenario and is validated by comparison with state-of-art active learning techniques originally developed for multiclass problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IGARSS.2012.6351934 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
data acquisition,geophysical image processing,learning (artificial intelligence),pattern classification,remote sensing,support vector machines,SVDD classifier training,active learning strategy,change detection scenario,diversity criterion,labeled sample acquisition,nontarget examples,one-class classification,remote sensing images,sample labeling,search efficiency,single-class discovery,supervised target detection,support vector data description,training sample identification,unlabeled sample selection | Small number,Semi-supervised learning,Change detection,Active learning (machine learning),Computer science,Remote sensing,Artificial intelligence,Classifier (linguistics),Active learning,Pattern recognition,Data acquisition,Support vector machine,Machine learning | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4673-1158-8 | 978-1-4673-1158-8 | 2 |
PageRank | References | Authors |
0.38 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Furlani, M. | 1 | 2 | 0.38 |
| Devis Tuia | 2 | 1715 | 101.88 |
| Munoz-Mari, J. | 3 | 9 | 1.65 |
| Bovolo, F. | 4 | 48 | 3.27 |