Abstract
Recent works on image co-segmentation aim to segment common objects among image sets. These methods can co-segment simple images well, but their performance may degrade significantly on more cluttered images. In order to co-segment both simple and complex images well, this paper proposes a novel paradigm to rank images and to propagate the segmentation results from the simple images to more and more complex ones. In the experiments, the proposed paradigm demonstrates its effectiveness in segmenting large image sets with a wide variety in object appearance, sizes, orientations, poses, and multiple objects in one image. It outperformed the current state-of-the-art algorithms significantly, especially in difficult images.
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