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Constrained Directed Graph Clustering and Segmentation Propagation for Multiple Foregrounds Cosegmentation

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Original languageEnglish
Pages (from-to)1735-1748
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number11
Publication statusPublished - 2015
Publication typeA1 Journal article-refereed


This paper proposes a new constrained directed graph clustering (DGC) method and segmentation propagation method for the multiple foreground cosegmentation. We solve the multiple object cosegmentation with the perspective of classification and propagation, where the classification is used to obtain the object prior of each class and the propagation is used to propagate the prior to all images. In our method, the DGC method is designed for the classification step, which adds clustering constraints in cosegmentation to prevent the clustering of the noise data. A new clustering criterion such as the strongly connected component search on the graph is introduced. Moreover, a linear time strongly connected component search algorithm is proposed for the fast clustering performance. Then, we extract the object priors from the clusters, and propagate these priors to all the images to obtain the foreground maps, which are used to achieve the final multiple objects extraction. We verify our method on both the cosegmentation and clustering tasks. The experimental results show that the proposed method can achieve larger accuracy compared with both the existing cosegmentation methods and clustering methods.


  • Graph clustering, Segmentation Propagation, Foreground segmentation

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