@conference {Vicente2011,
	title = {Object cosegmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2011},
	pages = {2217{\textendash}2224},
	abstract = {Cosegmentation is typically defined as the task of jointly segmenting something similar in a given set of images. Existing methods are too generic and so far have not demonstrated competitive results for any specific task. In this paper we overcome this limitation by adding two new aspects to cosegmentation: (1) the "something" has to be an object, and (2) the "similarity" measure is learned. In this way, we are able to achieve excellent results on the recently introduced iCoseg dataset, which contains small sets of images of either the same object instance or similar objects of the same class. The challenge of this dataset lies in the extreme changes in viewpoint, lighting, and object deformations within each set. We are able to considerably outperform several competitors. To achieve this performance, we borrow recent ideas from object recognition: the use of powerful features extracted from a pool of candidate object-like segmentations. We believe that our work will be beneficial to several application areas, such as image retrieval. {\textcopyright} 2011 IEEE.},
	isbn = {9781457703942},
	issn = {10636919},
	doi = {10.1109/CVPR.2011.5995530},
	author = {Vicente, Sara and Carsten Rother and Kolmogorov, Vladimir}
}
@conference {Vicente2010,
	title = {Cosegmentation revisited: Models and optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6312 LNCS},
	number = {PART 2},
	year = {2010},
	pages = {465{\textendash}479},
	abstract = {The problem of cosegmentation consists of segmenting the same object (or objects of the same class) in two or more distinct images. Recently a number of different models have been proposed for this problem. However, no comparison of such models and corresponding optimization techniques has been done so far. We analyze three existing models: the L1 norm model of Rother et al. [1], the L2 norm model of Mukherjee et al. [2] and the "reward" model of Hochbaum and Singh [3]. We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation [4]. In terms of optimization, we use a Dual Decomposition (DD) technique in addition to optimization methods in [1,2]. Experiments show a significant improvement of DD over published methods. Our main conclusion, however, is that the new model is the best overall because it: (i) has fewest parameters; (ii) is most robust in practice, and (iii) can be optimized well with an efficient EM-style procedure. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {3642155510},
	issn = {16113349},
	doi = {10.1007/978-3-642-15552-9_34},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Vicente2009,
	title = {Joint optimization of segmentation and appearance models},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {755{\textendash}762},
	abstract = {Many interactive image segmentation approaches use an objective function which includes appearance models as an unknown variable. Since the resulting optimization problem is NP-hard the segmentation and appearance are typically optimized separately, in an EM-style fashion. One contribution of this paper is to express the objective function purely in terms of the unknown segmentation, using higher-order cliques. This formulation reveals an interesting bias of the model towards balanced segmentations. Furthermore, it enables us to develop a new dual decomposition optimization procedure, which provides additionally a lower bound. Hence, we are able to improve on existing optimizers, and verify that for a considerable number of real world examples we even achieve global optimality. This is important since we are able, for the first time, to analyze the deficiencies of the model. Another contribution is to establish a property of a particular dual decomposition approach which involves convex functions depending on foreground area. As a consequence, we show that the optimal decomposition for our problem can be computed efficiently via a parametric maxflow algorithm. {\textcopyright}2009 IEEE.},
	isbn = {9781424444205},
	doi = {10.1109/ICCV.2009.5459287},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Vicente2008,
	title = {Graph cut based image segmentation with connectivity priors},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the "shrinking bias". To overcome this problem, we propose to impose an additional connectivity prior, which is a very natural assumption about objects. We formulate several versions of the connectivity constraint and show that the corresponding optimization problems are all NP-hard. For some of these versions we propose two optimization algorithms: (i) a practical heuristic technique which we call DijkstraGC, and (ii) a slow method based on problem decomposition which provides a lower bound on the problem. We use the second technique to verify that for some practical examples DijkstraGC is able to find the global minimum. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587440},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
