@string{accv="Proceedings of Asian Conference on Computer Vision"}
@string{lncs="Lecture Notes in Computer Science"}
@string{springer="Springer"}
@InProceedings{Kato-etal98,
  author =	 {Kato, Zoltan and Pong, Ting Chuen and Lee, John
                  Chung Mong},
  title =	 {Motion Compensated Color Video Classification Using
                  {M}arkov Random Fields},
  booktitle =	 accv,
  pages =	 {738--745},
  year =	 1998,
  editor =	 {Chin, Roland and Pong, Ting Chuen},
  volume =	 1351,
  series =	 lncs,
  address =	 {Hong Kong, China},
  month =	 jan,
  publisher =	 springer,
  pdf =		 {papers/accv98.pdf},
  ps =		 {papers/accv98.ps},
  abstract =	 {This paper deals with the classification of color
                  video sequences using Markov Random Fields (MRF)
                  taking into account motion information. The
                  theoretical framework relies on Bayesian estimation
                  associated with MRF modelization and combinatorial
                  optimization (Simulated Annealing). In the MRF
                  model, we use the CIE-luv color metric
                  because it is close to human perception when
                  computing color differences. In addition, intensity
                  and chroma information is separated in this
                  space. The sequence is regarded as a stack of frames
                  and both intra- and inter-frame cliques are defined
                  in the label field. Without motion compensation, an
                  inter-frame clique would contain the corresponding
                  pixel in the previous and next frame. In the motion
                  compensated model, we add a displacement field and
                  it is taken into account in inter-frame
                  interactions. The displacement field is also a MRF
                  but there are no inter-frame cliques. The Maximum A
                  Posteriori (MAP) estimate of the label and
                  displacement field is obtained through Simulated
                  Annealing. Parameter estimation is also considered
                  in the paper and results are shown on color video
                  sequences using both the simple and motion
                  compensated models.}
}