This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication

Year(s) from:  to 
Keywords (separated by spaces):

Are Spatial and Global Constraints Really Necessary for Segmentation?

Aurélien Lucchi, Yunpeng Li, Xavier Boix, Kevin Smith and Pascal Fua.
13th IEEE International Conference on Computer Vision (ICCV)
November 2011


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 performance increase attributed to the introduction of such constraints. On the contrary, we found that similar levels of performance can be achieved using a much simpler design that essentially ignores these constraints. This more simple approach makes use of the same local and global features to leverage evidence from the image, but instead directly biases the preferences of individual pixels. While our investigation does not prove that spatial and consistency constraints are not useful in principle, it points to the conclusion that they should be validated in a larger context.

Download in pdf format
  author = {AurĂ©lien Lucchi and Yunpeng Li and Xavier Boix and Kevin Smith and Pascal Fua.},
  title = {Are Spatial and Global Constraints Really Necessary for Segmentation?},
  booktitle = {13th IEEE International Conference on Computer Vision (ICCV)},
  year = {2011},
  month = {November},
  keywords = {}