[{"intvolume":"     10302","status":"public","volume":10302,"quality_controlled":"1","department":[{"_id":"VlKo"}],"publication_status":"published","publisher":"Springer","_id":"641","abstract":[{"text":"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.","lang":"eng"}],"date_created":"2018-12-11T11:47:39Z","date_published":"2017-01-01T00:00:00Z","month":"01","page":"323 - 334","publication_identifier":{"isbn":["978-331958770-7"]},"doi":"10.1007/978-3-319-58771-4_26","scopus_import":1,"language":[{"iso":"eng"}],"date_updated":"2021-01-12T08:07:23Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Trajkovska","first_name":"Vera","full_name":"Trajkovska, Vera"},{"full_name":"Swoboda, Paul","first_name":"Paul","last_name":"Swoboda","id":"446560C6-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Åström, Freddie","first_name":"Freddie","last_name":"Åström"},{"last_name":"Petra","first_name":"Stefanie","full_name":"Petra, Stefanie"}],"type":"conference","year":"2017","oa_version":"None","alternative_title":["LNCS"],"publist_id":"7147","day":"01","title":"Graphical model parameter learning by inverse linear programming","editor":[{"last_name":"Lauze","first_name":"François","full_name":"Lauze, François"},{"last_name":"Dong","first_name":"Yiqiu","full_name":"Dong, Yiqiu"},{"last_name":"Bjorholm Dahl","first_name":"Anders","full_name":"Bjorholm Dahl, Anders"}],"conference":{"location":"Kolding, Denmark","end_date":"2017-06-08","start_date":"2017-06-04","name":"SSVM: Scale Space and Variational Methods in Computer Vision"},"citation":{"ama":"Trajkovska V, Swoboda P, Åström F, Petra S. Graphical model parameter learning by inverse linear programming. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:323-334. doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">10.1007/978-3-319-58771-4_26</a>","apa":"Trajkovska, V., Swoboda, P., Åström, F., &#38; Petra, S. (2017). Graphical model parameter learning by inverse linear programming. In F. Lauze, Y. Dong, &#38; A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 323–334). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">https://doi.org/10.1007/978-3-319-58771-4_26</a>","short":"V. Trajkovska, P. Swoboda, F. Åström, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 323–334.","mla":"Trajkovska, Vera, et al. <i>Graphical Model Parameter Learning by Inverse Linear Programming</i>. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 323–34, doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">10.1007/978-3-319-58771-4_26</a>.","chicago":"Trajkovska, Vera, Paul Swoboda, Freddie Åström, and Stefanie Petra. “Graphical Model Parameter Learning by Inverse Linear Programming.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:323–34. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_26\">https://doi.org/10.1007/978-3-319-58771-4_26</a>.","ista":"Trajkovska V, Swoboda P, Åström F, Petra S. 2017. Graphical model parameter learning by inverse linear programming. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 323–334.","ieee":"V. Trajkovska, P. Swoboda, F. Åström, and S. Petra, “Graphical model parameter learning by inverse linear programming,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 323–334."}},{"publication_status":"published","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1703.03769"}],"oa":1,"volume":10302,"_id":"646","date_published":"2017-06-01T00:00:00Z","abstract":[{"text":"We present a novel convex relaxation and a corresponding inference algorithm for the non-binary discrete tomography problem, that is, reconstructing discrete-valued images from few linear measurements. In contrast to state of the art approaches that split the problem into a continuous reconstruction problem for the linear measurement constraints and a discrete labeling problem to enforce discrete-valued reconstructions, we propose a joint formulation that addresses both problems simultaneously, resulting in a tighter convex relaxation. For this purpose a constrained graphical model is set up and evaluated using a novel relaxation optimized by dual decomposition. We evaluate our approach experimentally and show superior solutions both mathematically (tighter relaxation) and experimentally in comparison to previously proposed relaxations.","lang":"eng"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T08:07:34Z","scopus_import":1,"publication_identifier":{"isbn":["978-331958770-7"]},"editor":[{"first_name":"François","full_name":"Lauze, François","last_name":"Lauze"},{"last_name":"Dong","full_name":"Dong, Yiqiu","first_name":"Yiqiu"},{"last_name":"Bjorholm Dahl","first_name":"Anders","full_name":"Bjorholm Dahl, Anders"}],"publist_id":"7132","year":"2017","oa_version":"Submitted Version","publisher":"Springer","department":[{"_id":"VlKo"}],"quality_controlled":"1","status":"public","intvolume":"     10302","page":"235 - 246","month":"06","date_created":"2018-12-11T11:47:41Z","project":[{"grant_number":"616160","name":"Discrete Optimization in Computer Vision: Theory and Practice","call_identifier":"FP7","_id":"25FBA906-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"doi":"10.1007/978-3-319-58771-4_19","ec_funded":1,"citation":{"short":"J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 235–246.","apa":"Kuske, J., Swoboda, P., &#38; Petra, S. (2017). A novel convex relaxation for non binary discrete tomography. In F. Lauze, Y. Dong, &#38; A. Bjorholm Dahl (Eds.) (Vol. 10302, pp. 235–246). Presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark: Springer. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">https://doi.org/10.1007/978-3-319-58771-4_19</a>","ama":"Kuske J, Swoboda P, Petra S. A novel convex relaxation for non binary discrete tomography. In: Lauze F, Dong Y, Bjorholm Dahl A, eds. Vol 10302. Springer; 2017:235-246. doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">10.1007/978-3-319-58771-4_19</a>","ista":"Kuske J, Swoboda P, Petra S. 2017. A novel convex relaxation for non binary discrete tomography. SSVM: Scale Space and Variational Methods in Computer Vision, LNCS, vol. 10302, 235–246.","ieee":"J. Kuske, P. Swoboda, and S. Petra, “A novel convex relaxation for non binary discrete tomography,” presented at the SSVM: Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 2017, vol. 10302, pp. 235–246.","chicago":"Kuske, Jan, Paul Swoboda, and Stefanie Petra. “A Novel Convex Relaxation for Non Binary Discrete Tomography.” edited by François Lauze, Yiqiu Dong, and Anders Bjorholm Dahl, 10302:235–46. Springer, 2017. <a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">https://doi.org/10.1007/978-3-319-58771-4_19</a>.","mla":"Kuske, Jan, et al. <i>A Novel Convex Relaxation for Non Binary Discrete Tomography</i>. Edited by François Lauze et al., vol. 10302, Springer, 2017, pp. 235–46, doi:<a href=\"https://doi.org/10.1007/978-3-319-58771-4_19\">10.1007/978-3-319-58771-4_19</a>."},"conference":{"location":"Kolding, Denmark","end_date":"2017-06-08","start_date":"2017-06-04","name":"SSVM: Scale Space and Variational Methods in Computer Vision"},"title":"A novel convex relaxation for non binary discrete tomography","day":"01","alternative_title":["LNCS"],"author":[{"last_name":"Kuske","full_name":"Kuske, Jan","first_name":"Jan"},{"first_name":"Paul","full_name":"Swoboda, Paul","last_name":"Swoboda","id":"446560C6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Petra","full_name":"Petra, Stefanie","first_name":"Stefanie"}],"type":"conference"}]