Multicue MRF Image Segmentation: Combining Texture and Color

Kato, Zoltan; Pong, Ting Chuen; Song, Guo Qiang

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.}
}