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Semi-supervised Probabilistic Relaxation for Image Segmentation

  • Adolfo Martínez-Usó
  • Filiberto Pla
  • José M. Sotoca
  • Henry Anaya-Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

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.

Keywords

Semi-supervised Image segmentation Probabilistic Relaxation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adolfo Martínez-Usó
    • 1
  • Filiberto Pla
    • 1
  • José M. Sotoca
    • 1
  • Henry Anaya-Sánchez
    • 1
  1. 1.Institute of New Imaging Technologies - Dept. of Computer Languages and SystemsUniversitat Jaume ICastellónSpain

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