Vector quantization and signal compression
Vector quantization and signal compression
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Relaxation using the Heat Equation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Data spectroscopy: learning mixture models using eigenspaces of convolution operators
Proceedings of the 25th international conference on Machine learning
Semisupervised Multitask Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Semi-supervised Gaussian Mixture Model for Image Segmentation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, a semi-supervised approach based on probabilistic relaxation theory is presented. It combines two desirable properties; firstly, a very small number of labelled samples is needed and, secondly, the assignment of labels is consistently performed according to our contextual information constraints. The proposed technique has been successfully applied to pattern recognition problems, obtaining promising preliminary results in database classification and image segmentation. Our methodology has also been evaluated against a recent state-of-the-art algorithm for semi-supervised learning, obtaining generally comparable or better results.