Stella X. Yu : Papers / Google Scholar

A Hierarchical Markov Random Field Model for Figure-Ground Segregation
Stella X. Yu and Tai Sing Lee and Takeo Kanade
Energy Minimization Methods in Computer Vision and Pattern Recognition, Sophia Antipolis, France, 3-5 Sept 2001
Paper | Slides

To segregate overlapping objects into depth layers requires the integration of local occlusion cues distributed over the entire image into a global percept. We propose to model this process using hierarchical Markov random field (HMRF), and suggest a broader view that clique potentials in MRF models can be used to encode any local decision rules. A topology-dependent multiscale hierarchy is used to introduce long range interaction. The operations within each level are identical across the hierarchy. The clique parameters that encode the relative importance of these decision rules are estimated using an optimization technique called learning from rehearsals based on 2-object training samples. We find that this model generalizes successfully to 5-object test images, and that depth segregation can be completed within two traversals across the hierarchy. This computational framework therefore provides an interesting platform for us to investigate the interaction of local decision rules and global representations, as well as to reason about the rationales underlying some of recent psychological and neurophysiological findings related to figure-ground segregation.

Markov Random Fields, figure-ground, depth segregation, learning from rehearsals