Iteratively reweighted graph cut for multi-label MRFs with non-convex priors
Date
2015
Authors
Ajanthan, Thalaiyasingam
Hartley, Richard
Salzmann, Mathieu
Li, Hongdong
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Publisher
IEEE
Abstract
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 a multi-label graph cut algorithm, and show that our algorithm then lets us handle of large variety of non-convex priors. We demonstrate the benefits of our method over state-of-the-art MRF energy minimization techniques on stereo and inpainting problems
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Source
Exemplar Hidden Markov Models for Classification of Facial Expressions in Videos
Type
Conference paper
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2037-12-31