Foreground-background segmentation using iterated distribution matching
This paper addresses the problem of image segmentation with a reference distribution. Recent studies have shown that segmentation with global consistency measures outperforms conventional techniques based on pixel-wise measures. However, such global approaches require a precise distribution to obtain the correct extraction. To overcome this strict assumption, we propose a new approach in which the given reference distribution plays a guiding role in inferring the latent distribution and its consistent region. The inference is based on an assumption that the latent distribution resembles the distribution of the consistent region but is distinct from the distribution of the complement region. We state the problem as the minimization of an energy function consisting of global similarities based on the Bhattacharyya distance and then implement a novel iterated distribution matching process for jointly optimizing distribution and segmentation. We evaluate the proposed algorithm on the GrabCut dataset, and demonstrate the advantages of using our approach with various segmentation problems, including interactive segmentation, background subtraction, and co-segmentation.