@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}
}
