@conference {Schlesinger2017,
	title = {Crowd sourcing image segmentation with iaSTAPLE},
	booktitle = {Proceedings - International Symposium on Biomedical Imaging},
	year = {2017},
	pages = {401{\textendash}405},
	abstract = {We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99\% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.},
	keywords = {Crowdsourcing, Epithelial cell segmentation, IaSTAPLE, Markovian Random Fields},
	isbn = {9781509011711},
	issn = {19458452},
	doi = {10.1109/ISBI.2017.7950547},
	author = {Schlesinger, Dmitrij and Jug, Florian and Myers, Gene and Carsten Rother and Kainmueller, Dagmar}
}
@conference {Kirillov2015a,
	title = {Inferring M-best diverse labelings in a single one},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {1814{\textendash}1822},
	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.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.211},
	author = {Kirillov, Alexander and Savchynskyy, Bogdan and Schlesinger, Dmitrij and Vetrov, Dmitry and Carsten Rother}
}
@conference {Kirillov2015,
	title = {M-best-diverse labelings for submodular energies and beyond},
	booktitle = {Advances in Neural Information Processing Systems},
	volume = {2015-Janua},
	year = {2015},
	pages = {613{\textendash}621},
	abstract = {We consider the problem of findingM best diverse solutions of energy minimization problems for graphical models. Contrary to the sequential method of Batra et al., which greedily finds one solution after another, we infer all M solutions jointly. It was shown recently that such jointly inferred labelings not only have smaller total energy but also qualitatively outperform the sequentially obtained ones. The only obstacle for using this new technique is the complexity of the corresponding inference problem, since it is considerably slower algorithm than the method of Batra et al. In this work we show that the joint inference of M best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used. In addition to the theoretical results we provide practical algorithms that outperform the current state-of-the-art and can be used in both submodular and non-submodular case.},
	issn = {10495258},
	author = {Kirillov, Alexander and Schlesinger, Dmitrij and Vetrov, Dmitry and Carsten Rother and Savchynskyy, Bogdan}
}
