In this paper, we formulate image co-segmentation as a classification problem in an unsupervised framework with the classes being the common foreground and the remaining regions in the image set. We first find a set of superpixels across all images with high feature similarity such that the constituent superpixels in individual images are spatially compact and label them as seed for the common foreground. Those superpixels with high background probability are labeled as respective seeds for multiple background classes. Seed computation here is unsupervised and automated unlike some semi-supervised methods. Then, we compute discriminative features that separate the initially labeled classes using linear discriminant analysis. We use these features to perform spatially constrained label propagation and obtain labels for the unlabeled regions, and iterate this process till the seed regions grow to the common object. Experimental results demonstrate excellent robustness properties even while processing non-homogeneous image sets where the common object is present only in majority of the images.