by Zoltan Kato, Ting Chuen Pong, John Chung Mong Lee
Abstract:
An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes.
Reference:
Zoltan Kato, Ting Chuen Pong, John Chung Mong Lee, Color Image Segmentation and Parameter Estimation in a Markovian Framework, In Pattern Recognition Letters, volume 22, no. 3-4, pp. 309-321, 2001.
Bibtex Entry:
@string{pattreclet="Pattern Recognition Letters"}
@Article{Kato-etal2001,
author = {Kato, Zoltan and Pong, Ting Chuen and Lee, John
Chung Mong},
title = {Color Image Segmentation and Parameter Estimation in
a {M}arkovian Framework},
journal = pattreclet,
year = 2001,
volume = 22,
number = {3-4},
pages = {309--321},
month = mar,
keywords = {Unsupervised image segmentation, Color, Markov
random fields, Pixel classification, Parameter
estimation},
pdf = {papers/pattreclet2001.pdf},
abstract = {An unsupervised color image segmentation algorithm
is presented, using a Markov random field (MRF)
pixel classification model. We propose a new method
to estimate initial mean vectors effectively even if
the histogram does not have clearly distinguishable
peaks. The only parameter supplied by the user is
the number of classes.}
}