Are Spatial and Global Constraints Really Necessary for Segmentation?

Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can greatly increase its complexity. As a result, estimating the model parameters or computing the best maximum a posteriori (MAP) assignment becomes a computationally expensive task. In a series of experiments on the PASCAL and the MSRC datasets, we were unable to find evidence of a significant

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Aurelien Lucchi, Yunpeng Li, Xavier Boix, Kevin Smith, Pascal Fua
• Keywords: Computer Vision, Consistency Constraints, Global Consistency, ICCV 2011, Random Field Models