Skip to main content
Springer Nature Link
Log in

Shifting multi-hypergraphs via collaborative probabilistic voting

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Graphs are widely utilized to characterize the complex relationship among big data. Graph mode seeking is of great importance to many applications in data mining and machine learning era, and it attracts a number of approaches. Typically, existing methods, e.g., graph shift, focus on shifting vertices based on pairwise edges (i.e., an edge connecting two vertices) to find the cohesively dense subgraph. However, they overlooked the semantics of these subgraphs, resulting into undesirable results to the users in specific applications, e.g., saliency detection. In this paper, we propose a novel paradigm aimed at shifting high-order edges (i.e., hyperedges) to deliver graph modes, via a novel probabilistic voting strategy. As a result, the generated graph modes based on dense subhypergraphs may more accurately capture the semantics of objects besides the self-cohesiveness requirement. It is widely known that data objects are always described by multiple features or multi-views, e.g., an image has a color feature and shape feature, where the information provided by all views are complementary to each other. Based on such fact, we propose another novel technique of shifting multiple hypergraphs, each of which corresponds to one view, by conducting a novel collaborative probabilistic voting strategy, named SMHCPV, so as to further improve the performance over hypergraph shift method. Extensive experiments are conducted on both synthetic and real-world datasets to validate the superiority of our proposed technique for both hypergraph shift and SMHCPV.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. http://www.cs.toronto.edu/~dmac/ShapeMatcher/index.html.

References

  1. Kim S, Nowozin S, Kohli P, Yoo CD (2011) Higher-order correlation clustering for image segmentation. In: Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira FCN, Weinberger KQ (eds) Advances in neural information processing systems 24: 25th annual conference on neural information processing systems 2011. Proceedings of a meeting held 12–14 Dec 2011, Granada, Spain

  2. Cho M, Lee KM (2012) Progressive graph matching: making a move of graphs via probabilistic voting. In: 2012 IEEE conference on computer vision and pattern recognition, Providence, RI, USA, 16–21 June 2012

  3. Pavan M, Pelillo M (2007) Dominant sets and pairwise clustering. IEEE Trans Pattern Anal Mach Intell 29(1):167–171

    Article  Google Scholar 

  4. Cho M, Lee KM (2012) Mode-seeking on graphs via random walks. In: 2012 IEEE conference on computer vision and pattern recognition, Providence, RI, USA, 16–21 June 2012

  5. Wu L, Wang Y, Shepherd J (2013) Efficient image and tag co-ranking: a bregman divergence optimization method. In: Jaimes A, Sebe N, Boujemaa N, Gatica-Perez D, Shamma DA, Worring M, Zimmermann R (eds) ACM multimedia conference, MM ’13. Barcelona, Spain, 21–25 Oct 2013

  6. Liu H, Yan S (2010) Robust graph mode seeking by graph shift. In: Frnkranz J, Joachims T (eds) Proceedings of the 27th international conference on machine learning (ICML-10), June 21–24, 2010. Haifa, Israel, pp 671–678

  7. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  8. Wang Y, Huang X, Wu L (2013) Clustering via geometric median shift over Riemannian manifolds. Inf Sci 20:292–305

    Article  MathSciNet  Google Scholar 

  9. Wang Y, Huang X (2010) Geometric median-shift over riemannian manifolds. In: Zhang B-T, Orgun MA (eds) PRICAI 2010: trends in artificial intelligence, 11th Pacific Rim international conference on artificial intelligence, Daegu, Korea, August 30–September 2, 2010. Proceedings. pp 268–279

  10. Sheikh Y, Khan EA, Kanade T (2007) Mode seeking by medoidshifts. In: IEEE 11th international conference on computer vision, ICCV 2007, Rio de Janeiro, Brazil, 14–20 Oct 2007

  11. Wang Y, Wu L, Huang X, Lin X (2012) Human action recognition from video sequences by enforcing tri-view constraints. Comput J 55(9):1030–1040

    Article  Google Scholar 

  12. Wang Y, Lin X, Zhang Q (2013) Towards metric fusion on multi-view data: a cross-view based graph random walk approach. In: He Q, Iyengar A, Nejdl W, Pei J, Rastogi R (eds) 22nd ACM international conference on information and knowledge management, CIKM’13, San Francisco, CA, USA, October 27–November 1, 2013

  13. Wang Y, Lin X, Wu L et al (2014) Exploiting correlation consensus: towards subspace clustering for multi-modal data. In: 22nd ACM international conference on multimedia, ACM Multimedia’14, Orlando, FL, USA, 3–7 Nov 2014

  14. Wang Y, Pei J, Lin X, Zhang Q, Zhang W (2014) An iterative fusion approach to graph-based semi-supervised learning from multiple views. In: Tseng VS, Ho TB, Zhou Z-H, Chen ALP, Kao H-Y (eds) Advances in knowledge discovery and data mining—18th Pacific-Asia conference, PAKDD 2014, Tainan, Taiwan, 13–16 May 2014. Proceedings, part II

  15. Wang Y, Cheema MA, Lin X, Zhang Q (2013) Multi-manifold ranking: using multiple features for better image retrieval. In: Pei J, Tseng VS, Cao L, Motoda H, Xu G (eds) Advances in knowledge discovery and data mining, 17th Pacific-Asia conference, PAKDD 2013, Gold Coast, Australia, 14–17 April 2013. Proceedings, part II

  16. Wang Y, Lin X, Zhang Q, Wu L (2014) Shifting hypergraphs by probabilistic voting. In: Tseng VS, Ho TB, Zhou Z-H, Chen ALP, Kao H-Y (eds) Advances in knowledge discovery and data mining—18th Pacific-Asia conference, PAKDD 2014, Tainan, Taiwan, May 13–16, 2014, pp 234–246

  17. Huang Y, Liu Q, Zhang S, Metaxas DN (2010) Image retrieval via probabilistic hypergraph ranking. In: The twenty-third IEEE conference on computer vision and pattern recognition, CVPR 2010, San Francisco, CA, USA, 13–18 June 2010

  18. Ding L, Yilmaz A (2010) Parallel mining of association rules. Pattern Recognit 43(5):1863–1873

    Article  MATH  Google Scholar 

  19. Bomze IM (2002) Branch-and-bound approaches to standard quadratic optimization problems. J Glob Optim 22(1–4):17–37

    Article  MathSciNet  MATH  Google Scholar 

  20. Weibull JW (1995) Evolutionary game theory. MIT Press, Cambridge 1995

    MATH  Google Scholar 

  21. Zangwill WI (1969) Nonlinear programming: a unified approach. Pretice-Hall press, Upper Saddle River

    MATH  Google Scholar 

  22. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the international conference on computer vision, Kerkyra, Corfu, Greece, 20–25 Sept 1999

  23. Boureau Y-L, Bach F, LeCun Y, Ponce J (2010) Learning mid-level features for recognition. In: The twenty-third IEEE conference on computer vision and pattern recognition, CVPR 2010, San Francisco, CA, USA, 13–18 June 2010

  24. Duchenme O, Bach F, Kweon I, Ponce J (2009) A tensor-based algorithm for high-order graph matching. In: 2009 IEEE computer society conference on computer vision and pattern recognition (CVPR 2009), Miami, FL, USA, 20–25 June 2009

  25. Zass R, Shashua A (2008) Probabilistic graph and hypergraph matching. In: 2008 IEEE computer society conference on computer vision and pattern recognition (CVPR 2008), Anchorage, Alaska, USA, 24–26 June 2008

  26. Leoradeanu M, Hebert M (2005) A spectral technique for correspondence problems using pairwise constraints. In: Carey MJ, Schneider DA (eds) 10th IEEE international conference on computer vision (ICCV 2005), Beijing, China, 17–20 Oct 2005

  27. Leordeanu M, Hebert M, Sukthankar R (2009) Integer projected fixed point for graph matching and map inference. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22: 23rd annual conference on neural information processing systems 2009. Proceedings of a meeting held 7–10 December 2009, Vancouver, BC, Canada

  28. Leordeanu M, Sminchisescu C (2012) Efficient hypergraph clustering. In: Lawrence ND, Girolami M (eds) Proceedings of the fifteenth international conference on artificial intelligence and statistics, AISTATS 2012, La Palma, Canary Islands, April 21–23, 2012, pp 676–684

  29. Goferman S, Zelnik-Manor L, Tal A (2010) Context-aware saliency detection. In: The twenty-third IEEE conference on computer vision and pattern recognition, CVPR 2010, San Francisco, CA, USA, 13–18 June 2010

  30. Cheng M-M, Zhang G-X, Mitra NJ, Huang X, Hu S-M (2011) Global contrast based salient region detection. In: The 24th IEEE conference on computer vision and pattern recognition, CVPR 2011, Colorado Springs, CO, USA, 20–25 June 2011

  31. Liu H, Latecki L, Yan S (2010) Robust clustering as ensembles of affinity relations. In: Lafferty JD, Williams CKI, Shawe-Taylor J, Zemel RS, Culotta A (eds) Advances in neural information processing systems 23: 24th annual conference on neural information processing systems 2010. Proceedings of a meeting held 6–9 December 2010, Vancouver, BC, Canada

  32. Bulo S, Pellilo M (2009) A game-theoretic approach to hypergraph clustering. In: Bengio Y, Schuurmans D, Lafferty JD, Williams CKI, Culotta A (eds) Advances in neural information processing systems 22: 23rd annual conference on neural information processing systems 2009. Proceedings of a meeting held 7–10 December 2009, Vancouver, BC, Canada

  33. Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2012) The PASCAL visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html

  34. Macrini D, Siddiqi K, Dickinson S (2008) From skeletons to bone graphs: medial abstractions for object recognition. In: 2008 IEEE computer society conference on computer vision and pattern recognition (CVPR 2008), 24–26 June 2008, Anchorage, Alaska, USA

Download references

Acknowledgments

Xuemin Lin’ s research is supported by ARC DP150102728, ARC DP140103578 and NSFC61021004. Wenjie Zhang is supported by ARC DP150103071 and ARC DP150102728.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, The University of New South Wales, Kensington, Sydney, NSW, 2052, Australia

    Yang Wang, Xuemin Lin, Lin Wu, Qing Zhang & Wenjie Zhang

  2. Australian Centre for Visual Technologies, University of Adelaide, Adelaide, SA, Australia

    Lin Wu

  3. Australia E-Health Research Centre, Brisbane, QLD, Australia

    Yang Wang & Qing Zhang

Authors
  1. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuemin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenjie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yang Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Lin, X., Wu, L. et al. Shifting multi-hypergraphs via collaborative probabilistic voting. Knowl Inf Syst 46, 515–536 (2016). https://doi.org/10.1007/s10115-015-0833-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-015-0833-8

Keywords