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A Semi-Supervised Gaussian Mixture Model for Image Segmentation

11 years 7 months ago
A Semi-Supervised Gaussian Mixture Model for Image Segmentation
In this paper, the results of a semi-supervised approach based on the Expectation-Maximisation algorithm for model-based clustering are presented. We show in this work that, if the appropriate generative model is chosen, the classification accuracy on clustering for image segmentation can be significantly improved by the combination of a reduced set of labelled data and a large set of unlabelled data. This technique has been tested on real images as well as on medical images from a dermatology application. The preliminary results are quite promising. Not only the unsupervised accuracies have been improved as expected but the segmentation results obtained are considerably better than the results obtained by other powerful and well-known unsupervised image segmentation techniques.
Adolfo Martínez-Usó, F. Pla, Jose Martínez Soto
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where ICPR
Authors Adolfo Martínez-Usó, F. Pla, Jose Martínez Sotoca
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