research-article

A site entropy rate and degree centrality based algorithm for image co-segmentation

Authors:

Souradeep Chakraborty,

Pabitra MitraAuthors Info & Claims

Journal of Visual Communication and Image Representation, Volume 33, Issue C

Pages 20 - 30

https://doi.org/10.1016/j.jvcir.2015.08.016

Published: 01 November 2015 Publication History

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.

References

[1]

F. Jing, M. Li, H.J. Zhang, B. Zhang, Relevance feedback in region-based image retrieval, IEEE Trans. Circuits Syst. Video Technol., 14 (2004) 672-681.

Digital Library

[2]

H. Li, K.N. Ngan, Q. Liu, FaceSeg: automatic face segmentation for real-time video, IEEE Trans. Multimedia, 11 (2009) 77-88.

Digital Library

[3]

Y.Y. Boykov, M.P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images, in: Proc. Int. Conf. Computer Vision, vol. 1, pp. 105-112.

[4]

J. Zhang, J. Zheng, J. Cai, A diffusion approach to seeded image segmentation, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010, pp. 2125-2132.

[5]

P. Rantalankila, J. Kannala, E. Rahtu, Generating object segmentation proposals using global and local search, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2014.

Digital Library

[6]

N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern., 9 (1979) 62-66.

[7]

X. Cao, Z. Tao, B. Zhang, H. Fu, W. Feng, Self-adaptively weighted co-saliency detection via rank constraint, IEEE Trans. Image Process. (T-IP), 23 (2014) 4175-4186.

[8]

C. Rother, V. Kolmogorov, T. Minka, A. Blake, Co-segmentation of image pairs by histogram matching - incorporating a global constraint into MRFs, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, New York, USA, 2006, pp. 993-1000.

Digital Library

[9]

L. Mukherjee, V. Singh, C. Dyer, Half-integrality based algorithms for co-segmentation of images, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, USA, 2009, pp. 2028-2035.

[10]

D. Hochbaum, V. Singh, An efficient algorithm for co-segmentation, in: Proc. Int. Conf. Computer Vision, Kyoto, Japan, 2009, pp. 269-276.

[11]

Y. Long, Y. Huang, Image based source camera identification using demosaicking, in: Proc. IEEE Eighth Workshop on Multimedia Signal Processing, Victoria, Canada, 2006, pp. 419-424.

[12]

K. Chang, T. Liu, S. Lai, From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Colorado Springs, USA, 2011, pp. 2129-2136.

Digital Library

[13]

S. Vicente, V. Kolmogorov, C. Rother, Co-segmentation revisited: models and optimization, in: Proc. Eur. Conf. Computer Vision, 2010, pp. 465-479.

Digital Library

[14]

A. Joulin, F. Bach, J. Ponce, Discriminative clustering for image co-segmentation, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010, pp. 1943-1950.

[15]

D. Batra, A. Kowdle, D. Parikh, Icoseg: interactive co-segmentation with intelligent scribble guidance, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2010, pp. 3169-3176.

[16]

L. Mukherjee, V. Singh, J. Peng, Scale invariant co-segmentation for image groups, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2011, pp. 1881-1888.

Digital Library

[17]

S. Vicente, C. Rother, V. Kolmogorov, Object co-segmentation, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2011, pp. 2217-2224.

Digital Library

[18]

G. Kim, E.P. Xing, L. Fei-Fei, T. Kanade, Distributed co-segmentation via submodular optimization on anisotropic diffusion, in: Proc. Int. Conf. Computer Vision, November 2011, pp. 169-176.

Digital Library

[19]

F. Meng, H. Li, G. Liu, K.N. Ngan, Object co-segmentation based on shortest path algorithm and saliency model, IEEE Trans. Multimedia, 14 (2012) 1429-1441.

Digital Library

[20]

J. Rubio, J. Serrat, A. Lopez, N. Paragios, Unsupervised co-segmentation through region matching, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, Providence, USA, 2012, pp. 749-756.

Digital Library

[21]

F. Meng, H. Li, G. Liu, K.N. Ngan, Image co-segmentation by incorporating color reward strategy and active contour model, IEEE Trans. Cybern., 43 (2013).

[22]

W. Tao, K. Li, K. Sun, SaCoseg: object co-segmentation by shape conformability, IEEE Trans. Image Process., 24 (2015) 943-955.

[23]

Q. Li, Y. Zhou, J. Yang, Saliency based image segmentation, in: International Conference on Multimedia Technology, 2011, pp. 5068-5071.

[24]

K.Y. Chang, T.L. Liu, S.H. Lai, From co-saliency to co-segmentation: an efficient and fully unsupervised energy minimization model, in: IEEE Int. Conference on Comput. Vision Pattern Recognit., June 2011, pp. 2129-2136.

Digital Library

[25]

G. Sharma, F. Jurie, C. Schmid, Discriminative spatial saliency for image classification, in: IEEE Conf. Computer Vision and Pattern Recognition, June 2012, pp. 3506-3513.

Digital Library

[26]

J. Huang, X. Yang, X. Fang, W. Lin, R. Zhang, Integrating visual saliency and consistency for re-ranking image search results, IEEE Trans. Multimedia, 13 (2011) 653-661.

Digital Library

[27]

C. Zhang, W. Lin, W. Li, B. Zhou, J. Xie, J. Li, Improved image deblurring based on salient-region segmentation, Signal Proc.: Image Commun., 28 (2013) 1171-1186.

Digital Library

[28]

C. Yang, L. Zhang, H. Lu, X. Ruan, M.-H. Yang, Saliency detection via graph-based manifold ranking, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2013, pp. 3166-3173.

Digital Library

[29]

Q. Yan, L. Xu, J. Shi, J. Jia, Hierarchical saliency detection, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2013, pp. 1155-1162.

Digital Library

[30]

H. Fu, X. Cao, Z. Tu, Cluster-based co-saliency detection, in: IEEE Trans. Image Process. vol. 22(10), pp. 3766-3778.

Digital Library

[31]

R. Pal, A. Mukherjee, P. Mitra, J. Mukherjee, Modeling visual saliency using degree centrality, IET Comput. Vis., 4 (2010) 218-229.

[32]

W. Wang, Y. Wang, Q. Huang, W. Gao, Measuring visual saliency by site entropy rate, in: Proc. IEEE Int. Conference on Comput. Vision Pattern Recognit., June 2010, pp. 2368-2375.

[33]

F. Meng, H. Li, K. Ngan, B. Zeng, N. Rao, Co-segmentation from Similar Backgrounds, in: IEEE International Symposium on Circuits and Systems (ISCAS), 2014, pp. 353-356.

[34]

M. Nilsback, A. Zisserman, A visual vocabulary for flower classification, in: Proc. IEEE Conf. Computer Vision and Pattern Recognition, New York, USA, 2006, pp. 1447-1454.

Digital Library

[35]

E. Borenstein, E. Sharon, S. Ullman, Combining top-down and bottom-up segmentation, in: IEEE Conf. Computer Vision and Pattern Recognition (CVPR), June 2004.

Digital Library

[36]

F. Meng, B. Luo, C. Huang, Object co-segmentation based on directed graph clustering, in: Conf. Visual Communications and Image Processing (VCIP), November 2013, pp. 1-5.

Cited By

Chakraborty SSamaras D(2024)Self-supervised Co-salient Object Detection via Feature Correspondences at Multiple ScalesComputer Vision – ECCV 202410.1007/978-3-031-72673-6_13(231-250)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72673-6_13
Han BSim J(2017)Saliency detection for panoramic landscape images of outdoor scenesJournal of Visual Communication and Image Representation10.5555/3163595.316381249:C(27-37)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.5555/3163595.3163812

A site entropy rate and degree centrality based algorithm for image co-segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems

Recommendations

Unsupervised video co-segmentation based on superpixel co-saliency and region merging

Nowadays, fully unsupervised video object segmentation is still a challenge in computer vision. Furthermore, it is more difficult to segment the object from a set of clips. In this paper, we propose an unsupervised and on-line method that efficiently ...
Hybrid PET/MRI co-segmentation based on joint fuzzy connectedness and graph cut

Tumor segmentation from hybrid PET/MRI scans.Iterative Relative Fuzzy Connectedness (IRFC) optimizes seeds initialization.Min-cut/max-flow technique allows the boundary smoothing.A visibility weighting scheme was adapted to achieve the task of co-...
An improved approach of lung image segmentation based on watershed algorithm
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service

As a preprocessing step of chest Computed Tomography (CT) images, lung segmentation is significant for the diagnosis of lung disease. The traditional watershed algorithm is sensitive to the noise and has the drawback of over-segmentation problem. This ...

Comments

Information & Contributors

Information

Published In

cover image Journal of Visual Communication and Image Representation

Journal of Visual Communication and Image Representation Volume 33, Issue C

November 2015

398 pages

ISSN:1047-3203

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 November 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chakraborty SSamaras D(2024)Self-supervised Co-salient Object Detection via Feature Correspondences at Multiple ScalesComputer Vision – ECCV 202410.1007/978-3-031-72673-6_13(231-250)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72673-6_13
Han BSim J(2017)Saliency detection for panoramic landscape images of outdoor scenesJournal of Visual Communication and Image Representation10.5555/3163595.316381249:C(27-37)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.5555/3163595.3163812

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents