Abstract

Recent advances in the joint processing of images have certainly shown its advantages over the individual processing. Different from the existing works geared towards cosegmentation or co-localization, in this paper, we explore a new joint processing topic: co-skeletonization, which is defifined as joint skeleton extraction of common objects in a set of semantically similar images. Object skeletonization in real world images is a challenging problem, because there is no prior knowledge of the object’s shape if we consider only a single image. This motivates us to resort to the idea of object co-skeletonization hoping that the commonness prior existing across the similar images may help, just as it does for other joint processing problems such as cosegmentation. Noting that skeleton can provide good scribbles for segmentation, and skeletonization, in turn, needs good segmentation, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefifit each other synergistically. Since it is a new problem, we also construct a benchmark dataset for the co-skeletonization task. Extensive experiments demonstrate that proposed method achieves very competitive results.