Comparison of linear discriminant functions in image classification
Articles
Lijana Stabingienė
Klaipeda University
Giedrius Stabingis
Klaipeda University
Kęstutis Dučinskas
Klaipeda University
Published 2010-12-21
https://doi.org/10.15388/LMR.2010.42
PDF

Keywords

training sample
Markov Random Fields
spatial correlation

How to Cite

Stabingienė L., Stabingis G. and Dučinskas K. (2010) “Comparison of linear discriminant functions in image classification”, Lietuvos matematikos rinkinys, 51(proc. LMS), pp. 227–231. doi: 10.15388/LMR.2010.42.

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.

PDF
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Please read the Copyright Notice in Journal Policy