Unsupervised cosegmentation based on global clustering and saliency

This paper introduces a new method for unsupervised cosegmentation. Our method combines saliency information with a Global Clustering step, which reveals parts of the objects by detecting similar subregions across image collections, based on a low dimensional descriptor that includes color, texture and positional features. The saliency information is used to yield a classification of the global clusters into foreground and background and also classify regions not detected as global clusters into potential background or foreground. These four types of regions are the input seeds for a Graph Cuts procedure that computes the final cosegmentation. The Graph Cuts result can also be used to compute a refined version of the saliency information which enables us to define an iterative cosegmentation pipeline. Our framework produces remarkable results in comparison with state-of-the-art works, even in challenging datasets with illumination variance, occluded objects and identical background.