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Semi-supervised Probabilistic Relaxation for Image Segmentation
- Source :
- Pattern Recognition and Image Analysis ISBN: 9783642212567, IbPRIA
- Publication Year :
- 2011
- Publisher :
- Springer Berlin Heidelberg, 2011.
-
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.
- Subjects :
- business.industry
Segmentation-based object categorization
Small number
Probabilistic logic
Scale-space segmentation
Pattern recognition
Image segmentation
Relaxation theory
Machine learning
computer.software_genre
Contextual information
Relaxation (approximation)
Artificial intelligence
business
computer
Mathematics
Subjects
Details
- ISBN :
- 978-3-642-21256-7
- ISBNs :
- 9783642212567
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition and Image Analysis ISBN: 9783642212567, IbPRIA
- Accession number :
- edsair.doi...........c58c5bed7040ba64116e293173059b68