Iteratively reweighted graph cut for multi-label MRFs with non-convex priors
While widely acknowledged as highly effective in computer vision, multi-label MRFs with non-convex priors are difficult to optimize. To tackle this, we introduce an algorithm that iteratively approximates the original energy with an appropriately weighted surrogate energy that is easier to minimize. Our algorithm guarantees that the original energy decreases at each iteration. In particular, we consider the scenario where the global minimizer of the weighted surrogate energy can be obtained by...[Show more]
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|Source:||Exemplar Hidden Markov Models for Classification of Facial Expressions in Videos|
|01_Ajanthan_Iteratively_reweighted_graph_2015.pdf||463.26 kB||Adobe PDF||Request a copy|
|02_Ajanthan_Iteratively_reweighted_graph_2015.pdf||303.05 kB||Adobe PDF||Request a copy|
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