Graphical model parameter learning by inverse linear programming Conference Paper


Author(s): Trajkovska, Vera; Swoboda, Paul; Åström, Freddie; Petra, Stefanie
Title: Graphical model parameter learning by inverse linear programming
Title Series: LNCS
Affiliation IST Austria
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.
Conference Title: SSVM: Scale Space and Variational Methods in Computer Vision
Volume: 10302
Conference Dates: June 4 - 8, 2017
Conference Location: Kolding, Denmark
ISBN: 978-331958770-7
Publisher: Springer  
Date Published: 2017-01-01
Start Page: 323
End Page: 334
DOI: 10.1007/978-3-319-58771-4_26
Open access: no
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