Semi-supervised probabilistic relaxation for image segmentation
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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http://dx.doi.org/10.1007/978-3-642-21257-4 |
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Title
Semi-supervised probabilistic relaxation for image segmentationDate
2011Publisher
Springer-VerlagISSN
0302-9743Type
info:eu-repo/semantics/articlePublisher version
http://www.springerlink.com/content/7665350437031u83/fulltext.pdfAbstract
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 ... [+]
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 [-]
Is part of
Lecture notes in computer science (2011), vol. 6669, 428-435Rights
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