by Zoltan Kato, Ting Chuen Pong, Guo Qiang Song
Abstract:
We propose a new Markov random field (MRF) image segmentation model which aims at combining color and texture features. The model has a multilayer structure: each feature has its own layer, called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The uniqueness of our algorithm is that it provides both color only and texture only segmentations as well as a segmentation based on combined color and texture features. The number of classes on feature layers is given by the user, but it is estimated on the combined layer.
Reference:
Zoltan Kato, Ting Chuen Pong, Guo Qiang Song, Multicue MRF Image Segmentation: Combining Texture and Color, In Proceedings of International Conference on Pattern Recognition, volume 1, Quebec, Canada, pp. 660-663, 2002, IEEE.
Bibtex Entry:
@string{icpr="Proceedings of International Conference on Pattern Recognition"}
@InProceedings{Kato-etal2002,
author = {Kato, Zoltan and Pong, Ting Chuen and Song, Guo
Qiang},
title = {Multicue {MRF} Image Segmentation: Combining Texture
and Color},
booktitle = icpr,
year = 2002,
address = {Quebec, Canada},
month = aug,
organization = {IAPR},
publisher = {IEEE},
volume = {1},
pages = {660-663},
pdf = {papers/icpr2002.pdf},
ps = {papers/icpr2002.ps},
abstract = { We propose a new Markov random field (MRF) image
segmentation model which aims at combining color and
texture features. The model has a multilayer
structure: each feature has its own layer, called
feature layer, where an MRF model is defined using
only the corresponding feature. A special layer is
assigned to the combined MRF model. This layer
interacts with each feature layer and provides the
segmentation based on the combination of different
features. The uniqueness of our algorithm is that it
provides both color only and texture only
segmentations as well as a segmentation based on
combined color and texture features. The number of
classes on feature layers is given by the user, but
it is estimated on the combined layer.}
}