Description: In this paper, we address a challenging image segmentation
problem called multiple foreground cosegmentation
(MFC), which concerns a realistic scenario in general Webuser
photo sets where a finite number of K foregrounds of
interest repeatedly occur over the entire photo set, but only
an unknown subset of them is presented in each image. This
contrasts the classical cosegmentation problem dealt with
by most existing algorithms, which assume a much simpler
but less realistic setting where the same set of foregrounds
recurs in every image. We propose a novel optimization
method for MFC, which makes no assumption
on foreground configurations and does not suffer from the
aforementioned limitation, while still leverages all the benefits
of having co-occurring or (partially) recurring contents
across images. Our method builds on an iterative scheme
that alternates between a foreground modeling module and
a region assignment module, both highly efficient and scalable.
In part
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