Semi-supervised probabilistic relaxation for image segmentation

Authors:
Adolfo Martínez-Usó;Filiberto Pla;José M. Sotoca;Henry Anaya-Sánchez
Affiliations:
Institute of New Imaging Technologies, Dept. of Computer Languages and Systems, Universitat Jaume I, Castellón, Spain;Institute of New Imaging Technologies, Dept. of Computer Languages and Systems, Universitat Jaume I, Castellón, Spain;Institute of New Imaging Technologies, Dept. of Computer Languages and Systems, Universitat Jaume I, Castellón, Spain;Institute of New Imaging Technologies, Dept. of Computer Languages and Systems, Universitat Jaume I, Castellón, Spain
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

Citing 8
Cited 0

Structural Matching in Computer Vision Using Probabilistic Relaxation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust analysis of feature spaces: color image segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Theoretical Framework for Relaxation Processes in Pattern Recognition: Application to Robust Nonparametric Contour Generalization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation

International Journal of Computer Vision
Statistical Region Merging

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
A Semi-supervised Gaussian Mixture Model for Image Segmentation

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

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.