A trained spin-glass model for grouping of image primitives

A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2005-07, Vol.27 (7), p.1172-1182
Main Authors: Staal, J., Kalitzin, S.N., Viergever, M.A.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Bayesian grouping
Bayesian methods
Classification
Computer science
control theory
systems
Data mining
Digital images
Exact sciences and technology
Image analysis
Image segmentation
Index Terms- Statistical pattern recognition
Instability
Intelligence
Learning and adaptive systems
Mathematical models
Minima
Pattern analysis
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Probability
Rendering (computer graphics)
Retina
spin-glass model
Stability
Statistical learning
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, allowing interaction between the image primitives. Instead of defining the state of lowest energy as the grouping result, the mean state of the system is taken. In this way, instabilities caused by multiple minima in the energy are being avoided. The means of the spins are taken as the a posteriori probabilities for the grouping result. In the paper, it is shown how the energy functional can be learned from example data. The energy functional is defined in such a way that, in case of no interactions between the elements, the means of the spins equal the a priori local probabilities. The grouping process enables the fusion of the a priori local and bilocal probabilities into the a posteriori probabilities. The method is illustrated both on grouping of line elements in synthetic images and on vessel detection in retinal fundus images.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2005.131