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 cross 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 particular, our approach is flexible enough to integrate any advanced region classifiers for foreground modeling, and our region assignment employs a combinatorial auction framework that enjoys several intuitively good properties such as optimality guarantee and linear complexity. We show the superior performance of our method in both segmentation quality and scalability in comparison with other state-of-the-art techniques on a newly introduced FlickrMFC dataset and the standard ImageNet dataset.