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Comparison of linear discriminant functions in image classification
- Source :
- Lietuvos Matematikos Rinkinys, Vol 51, Iss proc. LMS (2010)
- Publication Year :
- 2010
- Publisher :
- Vilnius University Press, 2010.
-
Abstract
- In statistical image classification it is usually assumed that feature observations given labels are independently distributed. We have retracted this assumption by proposing stationary Gaussian random field (GRF) model for features observations. Conditional distribution of label of observation to be classified is assumed to be dependent on its spatial adjacency with training sample spatial framework. Perfomance of the Bayes discriminant function (BDF) and performance of plug-in BDF are tested and are compared with ones ignoring spatial correlation among feature observations.For illustration image of figure corrupted by additive GRF is analyzed. Advantage of proposed BDF against competing ones is shown visually and numerically.
- Subjects :
- training sample
Markov Random Fields
spatial correlation
Mathematics
QA1-939
Subjects
Details
- Language :
- English, Lithuanian
- ISSN :
- 01322818 and 2335898X
- Volume :
- 51
- Issue :
- proc. LMS
- Database :
- Directory of Open Access Journals
- Journal :
- Lietuvos Matematikos Rinkinys
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3d56c37e22b6472ca5d63be1969bb9cf
- Document Type :
- article
- Full Text :
- https://doi.org/10.15388/LMR.2010.42