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.
This work was supported by the Spanish Ministry of Science and Innovation under the projects Consolider Ingenio 2010 CSD2007-00018, AYA2008-05965-C04-04/ESP, TIN2009-14103-C03-01 and by Caixa-Castelló foundation under the projects P1 1B2009-45.
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Martínez-Usó, A., Pla, F., Sotoca, J.M., Anaya-Sánchez, H. (2011). Semi-supervised Probabilistic Relaxation for Image Segmentation. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_53
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DOI: https://doi.org/10.1007/978-3-642-21257-4_53
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