Unsupervised segmentation of color textured images using a multi-layer MRF model (bibtex)
by Zoltan Kato, Ting Chuen Pong, Guo Qiang Song
Abstract:
Herein, we propose a novel multilayer Markov random field (MRF) image segmentation model which aims at combining color and texture features: each feature is associated to a so 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 model is quite generic and isn't restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters.
Reference:
Zoltan Kato, Ting Chuen Pong, Guo Qiang Song, Unsupervised segmentation of color textured images using a multi-layer MRF model, In Proceedings of International Conference on Image Processing, volume I, Barcelona, Spain, pp. 961-964, 2003.
Bibtex Entry:
@string{icip="Proceedings of International Conference on Image Processing"}
@InProceedings{Kato-etal2003,
  author =	 {Kato, Zoltan and Pong, Ting Chuen and Song, Guo
                  Qiang},
  title =	 {Unsupervised segmentation of color textured images
                  using a multi-layer {MRF} model},
  booktitle =	 icip,
  year =	 2003,
  address =	 {Barcelona, Spain},
  month =	 sep,
  organization = {IEEE},
  volume =	 {I},
  pages =	 {961--964},
  pdf =		 {papers/icip2003.pdf},
  abstract =	 {Herein, we propose a novel multilayer Markov random
                  field (MRF) image segmentation model which aims at
                  combining color and texture features: each feature
                  is associated to a so 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 model is
                  quite generic and isn't restricted to a particular
                  texture feature. Herein we will test the algorithm
                  using Gabor and MRSAR texture features. Furthermore,
                  the algorithm automatically estimates the number of
                  classes at each layer (there can be different
                  classes at different layers) and the associated
                  model parameters.}
}
Powered by bibtexbrowser