Semi-supervised probabilistic relaxation for image segmentation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Semi-supervised classification by probabilistic relaxation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Supervised learning of Gaussian mixture models for visual vocabulary generation
Pattern Recognition
Semi-supervised discriminatively regularized classifier with pairwise constraints
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
| Hi-index | 0.00 |
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.