Abstract
We introduce two novel methods for learning parameters of graphical models for image labelling. The following two tasks underline both methods: (i) perturb model parameters based on given features and ground truth labelings, so as to exactly reproduce these labelings as optima of the local polytope relaxation of the labelling problem; (ii) train a predictor for the perturbed model parameters so that improved model parameters can be applied to the labelling of novel data. Our first method implements task (i) by inverse linear programming and task (ii) using a regressor e.g. a Gaussian process. Our second approach simultaneously solves tasks (i) and (ii) in a joint manner, while being restricted to linearly parameterised predictors. Experiments demonstrate the merits of both approaches.
Acknowledgments: VT and FÅ gratefully acknowledge support by the German Science Foundation, grant GRK 1653.
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- 1.
We do not denote the dual variables by \(\nu \), as in the preceding section, due to the slightly different LP formulation (10).
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Trajkovska, V., Swoboda, P., Åström, F., Petra, S. (2017). Graphical Model Parameter Learning by Inverse Linear Programming. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_26
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