Motion Compensated Color Video Classification Using Markov Random Fields

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 intraand 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.