%0 Conference Paper %1 7410568 %A Kirillov, Alexander %A Savchynskyy, Bogdan %A Schlesinger, Dmitrij %A Vetrov, Dmitry %A Rother, Carsten %B 2015 IEEE International Conference on Computer Vision (ICCV) %D 2015 %K Labeling;Graphical methods;Minimization;Computer modeling;Optimization models;Diversity vision;Computational %P 1814--1822 %R 10.1109/ICCV.2015.211 %T Inferring M-Best Diverse Labelings in a Single One %X We consider the task of finding M-best diverse solutions in a graphical model. In a previous work by Batra et al. an algorithmic approach for finding such solutions was proposed, and its usefulness was shown in numerous applications. Contrary to previous work we propose a novel formulation of the problem in form of a single energy minimization problem in a specially constructed graphical model. We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion. We evaluate our method on two application scenarios, interactive and semantic image segmentation, with binary and multiple labels. In both cases we achieve considerably better error rates than state-of-the art diversity methods. Furthermore, we empirically discover that in the binary label case we were able to reach global optimality for all test instances.

@inproceedings{7410568, abstract = {We consider the task of finding M-best diverse solutions in a graphical model. In a previous work by Batra et al. an algorithmic approach for finding such solutions was proposed, and its usefulness was shown in numerous applications. Contrary to previous work we propose a novel formulation of the problem in form of a single energy minimization problem in a specially constructed graphical model. We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion. We evaluate our method on two application scenarios, interactive and semantic image segmentation, with binary and multiple labels. In both cases we achieve considerably better error rates than state-of-the art diversity methods. Furthermore, we empirically discover that in the binary label case we were able to reach global optimality for all test instances.}, added-at = {2024-10-02T10:38:17.000+0200}, author = {Kirillov, Alexander and Savchynskyy, Bogdan and Schlesinger, Dmitrij and Vetrov, Dmitry and Rother, Carsten}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/220d317d3c0cd98bb5a69ce4fb318e3e5/scadsfct}, booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)}, doi = {10.1109/ICCV.2015.211}, interhash = {fd90392453ea4e42ef30d73c27698376}, intrahash = {20d317d3c0cd98bb5a69ce4fb318e3e5}, issn = {2380-7504}, keywords = {Labeling;Graphical methods;Minimization;Computer modeling;Optimization models;Diversity vision;Computational}, month = Dec, pages = {1814--1822}, timestamp = {2024-10-02T10:38:17.000+0200}, title = {Inferring M-Best Diverse Labelings in a Single One}, year = 2015 }