Name
Abstract | ||
|---|---|---|
In this paper, a semi-supervised approach based on probabilistic relaxation theory is presented. Focused on image segmentation, the presented technique combines two desirable properties; a very small number of labelled samples is needed and the assignment of labels is consistently performed according to our contextual information constraints. Our proposal has been tested on medical images from a dermatology application with quite promising preliminary results. Not only the unsupervised accuracies have been improved as expected but similar accuracies to other semi-supervised approach have been obtained using a considerably reduced number of labelled samples. Results have been also compared with other powerful and well-known unsupervised image segmentation techniques, improving significantly their results. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21257-4_53 | IbPRIA |
Keywords | Field | DocType |
dermatology application,well-known unsupervised image segmentation,contextual information constraint,semi-supervised probabilistic relaxation,unsupervised accuracy,labelled sample,desirable property,medical image,small number,semi-supervised approach,image segmentation | Small number,Computer vision,Contextual information,Scale-space segmentation,Pattern recognition,Computer science,Relaxation theory,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Probabilistic logic | Conference |
Volume | ISSN | Citations |
6669 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adolfo Martínez Usó | 1 | 152 | 9.88 |
| Filiberto Pla | 2 | 557 | 60.06 |
| José M. Sotoca | 3 | 13 | 2.03 |
| Henry Anaya-Sánchez | 4 | 58 | 6.52 |