Object co-segmentation based on directed graph clustering

In this paper, we develop a new algorithm to segment multiple common objects from a group of images. Our method consists of two aspects: directed graph clustering and prior propagation. The clustering is used to cluster the local regions of the original images and generate the foreground priors from these clusterings. The second step propagates the prior of each class and locates the common objects from the images in terms of foreground map. Finally, we use the foreground map as the unary term of Markov random field segmentation and segment the common objects by graph-cuts algorithm. We test our method on FlickrMFC and ICoseg datasets. The experimental results show that the proposed method can achieve larger accuracy compared with several state-of-arts co-segmentation methods.