Cosegmentation Revisited: Models and Optimization.
In: Daniilidis, K and Maragos, P and Paragios, N, (eds.)
(465 - 479).
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 Bother et al. , the L2 norm model of Mukherjee et al.  and the "reward" model of Hochbaum and Singh . We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation .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.
|Title:||Cosegmentation Revisited: Models and Optimization|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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