A site entropy rate and degree centrality based algorithm for image co-segmentation
•Site entropy rate of graph nodes can be utilized to segment common object in images.•Degree centrality of graph nodes can also be used to obtain co-segmented regions.•Guided by co-saliency map, we combine the two methods to get image co-segmentations.•The proposed algorithm co-segments multiple ins...
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| Published in | Journal of visual communication and image representation Vol. 33; pp. 20 - 30 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.11.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1047-3203 1095-9076 |
| DOI | 10.1016/j.jvcir.2015.08.016 |
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| Abstract | •Site entropy rate of graph nodes can be utilized to segment common object in images.•Degree centrality of graph nodes can also be used to obtain co-segmented regions.•Guided by co-saliency map, we combine the two methods to get image co-segmentations.•The proposed algorithm co-segments multiple instances of a common object in images.•The proposed algorithm co-segments images with multiple classes.
In this paper, we propose a graph based algorithm that efficiently segments common objects from multiple images. We first generate a number of object proposals from each image. Then, an undirected graph is constructed based on proposal similarities and co-saliency maps. Two different methods are followed to extract the proposals containing common objects. They are: (1) degree centrality of nodes obtained after graph thresholding and (2) site entropy rate of nodes calculated on the stationary distribution of Markov chain constructed on the graph. Finally, we obtain the co-segmented image region by selecting the more salient of the object proposals obtained by the two methods, for each image. Multiple instances of the common object are also segmented efficiently. The proposed method has been compared with many existing co-segmentation methods on three standard co-segmentation datasets. Experimental results show its effectiveness in co-segmentation, with larger IoU values as compared to other co-segmentation methods. |
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| AbstractList | •Site entropy rate of graph nodes can be utilized to segment common object in images.•Degree centrality of graph nodes can also be used to obtain co-segmented regions.•Guided by co-saliency map, we combine the two methods to get image co-segmentations.•The proposed algorithm co-segments multiple instances of a common object in images.•The proposed algorithm co-segments images with multiple classes.
In this paper, we propose a graph based algorithm that efficiently segments common objects from multiple images. We first generate a number of object proposals from each image. Then, an undirected graph is constructed based on proposal similarities and co-saliency maps. Two different methods are followed to extract the proposals containing common objects. They are: (1) degree centrality of nodes obtained after graph thresholding and (2) site entropy rate of nodes calculated on the stationary distribution of Markov chain constructed on the graph. Finally, we obtain the co-segmented image region by selecting the more salient of the object proposals obtained by the two methods, for each image. Multiple instances of the common object are also segmented efficiently. The proposed method has been compared with many existing co-segmentation methods on three standard co-segmentation datasets. Experimental results show its effectiveness in co-segmentation, with larger IoU values as compared to other co-segmentation methods. |
| Author | Mitra, Pabitra Chakraborty, Souradeep |
| Author_xml | – sequence: 1 givenname: Souradeep surname: Chakraborty fullname: Chakraborty, Souradeep email: sourachakra@gmail.com organization: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721302, India – sequence: 2 givenname: Pabitra surname: Mitra fullname: Mitra, Pabitra email: pabitra@gmail.com organization: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India |
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| Cites_doi | 10.1109/TSMC.1979.4310076 10.1109/ICCV.2011.6126239 10.1109/CVPR.2013.407 10.1109/CVPR.2010.5539927 10.1109/TMM.2008.2008922 10.1109/ISCAS.2014.6865138 10.1109/TCSVT.2004.826775 10.1109/CVPR.2012.6247745 10.1109/CVPR.2012.6248093 10.1109/VCIP.2013.6706376 10.1007/978-3-642-15552-9_34 10.1109/CVPR.2006.42 10.1109/CVPR.2013.153 10.1109/CVPR.2004.314 10.1109/MMSP.2006.285343 10.1109/ICCV.2001.937505 10.1109/TIP.2013.2260166 10.1109/CVPRW.2009.5206652 10.1109/CVPR.2006.91 10.1109/CVPR.2011.5995420 10.1109/CVPR.2011.5995415 10.1109/CVPR.2010.5539891 10.1109/ICCV.2009.5459261 10.1109/CVPR.2011.5995530 10.1109/TMM.2012.2197741 10.1109/CVPR.2010.5539868 10.1109/TMM.2011.2127463 10.1109/CVPR.2014.310 10.1049/iet-cvi.2009.0067 10.1109/TIP.2014.2387384 10.1109/ICMT.2011.6002178 10.1109/CVPR.2010.5540080 |
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| Keywords | Markov chain Site entropy rate Co-segmentation Co-saliency Degree centrality k-partite graph Object proposal Stationary distribution |
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| Snippet | •Site entropy rate of graph nodes can be utilized to segment common object in images.•Degree centrality of graph nodes can also be used to obtain co-segmented... |
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| SubjectTerms | Co-saliency Co-segmentation Degree centrality k-partite graph Markov chain Object proposal Site entropy rate Stationary distribution |
| Title | A site entropy rate and degree centrality based algorithm for image co-segmentation |
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