Tuesday, November 07, 2006

segmentation as inference in a graphical model

Recently, I've been playing with the idea of obtaining an image segmentation by inference on a random field. For a Probabilistic Graphical Models final class project, my teammate and I have been using Conditional Random Fields for segmentation. By posing segmentation as a superpixel labelling problem and placing a random field structure over the class posterior distribution, we were able to obtain cool looking segmentations.

On another note, did you know that logistic regression can be viewed as a conditional random field with one output variable? Once you see this, then you'll never forget why logistic regression looks the way it does. Maybe you should read this really cool CRF tutorial.