Skip to main content
SpringerLink
Log in

Supervised Interactive Co-segmentation Using Histogram Matching and Bipartite Graph Construction

  • Conference paper
  • First Online:
Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12047))

Included in the following conference series:

  • 1215 Accesses

Abstract

The identification and retrieval of images of same or similar objects finds application in various tasks that are of prime importance in Image Processing and Computer Vision. Accurate and fast extraction of the object of interest from several images is essential for the construction of 3D models, image retrieval applications. The joint partitioning of multiple images having same or similar objects of interest into background and foreground parts is referred to as co-segmentation.

This article proposes a novel and efficient interactive co-segmentation method based on the computation of a global energy function and a local smooth energy function. Computation of global energy function from the scribbled regions of the images is based on histogram matching. This is used to estimate the probability of each region belonging either to foreground or background region. The local smooth energy function is used to estimate the probability of regions having similar colour appearance. To further improve the quality of the segmentation, a bipartite graph is constructed using the segments. The algorithm has been implemented on iCoseg and MSRC benchmark data sets. The extensive experimental results show significant improvement in performance compared to many state-of-the-art unsupervised co-segmentation and supervised interactive co-segmentation methods, both in computational time and accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: iCoseg: interactive co-segmentation with intelligent scribble guidance. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 3169–3176, June 2010

    Google Scholar 

  2. Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: Interactively co-segmentating topically related images with intelligent scribble guidance. Int. J. Comput. Vis. 93(3), 273–292 (2011)

    Article  Google Scholar 

  3. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)

    Article  Google Scholar 

  4. Cheng, B., Liu, G., Wang, J., Huang, Z., Yan, S.: Multitask low-rank affinity pursuit for image segmentation. In: Proceedings of 2011 International Conference on Computer Vision, pp. 2439–2446, November 2011

    Google Scholar 

  5. Collins, M.D., Xu, J., Grady, L., Singh, V.: Random walks based multi-image segmentation: quasiconvexity results and GPU based solutions. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1656–1663, June 2012

    Google Scholar 

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

    Article  Google Scholar 

  7. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  8. Dong, X., Shen, J., Shao, L., Yang, M.H.: Interactive cosegmentation using global and local energy optimization. IEEE Trans. Image Process. 24(11), 3966–3977 (2015)

    Article  MathSciNet  Google Scholar 

  9. Faktor, A., Irani, M.: Co-segmentation by composition. In: Proceedings of 2013 IEEE International Conference on Computer Vision, pp. 1297–1304, December 2013

    Google Scholar 

  10. Fu, H., Xu, D., Lin, S., Liu, J.: Object-based RGBD image co-segmentation with mutex constraint. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4428–4436, June 2015

    Google Scholar 

  11. Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. In: Proceedings of 2009 IEEE 12th International Conference on Computer Vision, pp. 269–276, September 2009

    Google Scholar 

  12. Kim, T.H., Lee, K.M., Lee, S.U.: Non parametric higher-order learning for interactive segmentation. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010). https://doi.org/10.1109/CVPR.2010.5540078

  13. Joulin, A., Bach, F., Ponce, J.: Discriminative clustering for image co-segmentation. In: Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1943–1950, June 2010

    Google Scholar 

  14. Joulin, A., Bach, F., Ponce, J.: Multi-class cosegmentation. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 542–549, June 2012

    Google Scholar 

  15. Lou, Z., Gevers, T.: Extracting primary objects by video co-segmentation. IEEE Trans. Multimedia 16(8), 2110–2117 (2014)

    Article  Google Scholar 

  16. Mu, Y., Zhou, B.: Co-segmentation of image pairs with quadratic global constraint in MRFs. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007. LNCS, vol. 4844, pp. 837–846. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76390-1_82

    Chapter  Google Scholar 

  17. Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2028–2035, June 2009

    Google Scholar 

  18. Rother, C., Minka, T., Blake, A., Kolmogorov, V.: Cosegmentation of image pairs by histogram matching incorporating a global constraint into MRFs. In: Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, Washington, DC, USA, vol. 1, pp. 993–1000 (2006)

    Google Scholar 

  19. Rubio, J.C., Serrat, J., Lpez, A., Paragios, N.: Unsupervised co-segmentation through region matching. In: Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 749–756, June 2012

    Google Scholar 

  20. Shen, J., Du, Y., Wang, W., Li, X.: Lazy random walks for superpixel segmentation. IEEE Trans. Image Process. 23(4), 1451–1462 (2014)

    Article  MathSciNet  Google Scholar 

  21. Vicente, S., Rother, C., Kolmogorov, V.: Object cosegmentation. In: Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Washington DC, USA, pp. 2217–2224 (2011)

    Google Scholar 

  22. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Anal. Mach. Intell. 23(9), 947–963 (2001)

    Article  Google Scholar 

  23. Winn, J., Criminisi, A., Minka, T.: Object categorization by learned universal visual dictionary. In: Proceedings of 10th IEEE International Conference on Computer Vision, ICCV 2005, Washington DC, USA, vol. 2, pp. 1800–1807 (2005)

    Google Scholar 

  24. Yuille, A.L., Cohen, D.S., Hallinan, P.W.: Feature extraction from faces using deformable templates. In: Proceedings of Computer Vision and Pattern Recognition, CVPR 1989, pp. 104–109, June 1989

    Google Scholar 

  25. Zhu, H., Lu, J., Cai, J., Zheng, J., Thalmann, N.M.: Multiple foreground recognition and cosegmentation: an object-oriented CRF model with robust higher-order potentials. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, pp. 485–492, March 2014

    Google Scholar 

  26. Li, W., Hosseini Jafari, O., Rother, C.: Deep object co-segmentation. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 638–653. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_40

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Statistical Institute, 203, B.T. Road, Kolkata, 700108, India

    Harsh Bhandari, Sarbani Palit & Bhabatosh Chanda

Authors
  1. Harsh Bhandari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarbani Palit
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bhabatosh Chanda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarbani Palit .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bhandari, H., Palit, S., Chanda, B. (2020). Supervised Interactive Co-segmentation Using Histogram Matching and Bipartite Graph Construction. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-41299-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41298-2

  • Online ISBN: 978-3-030-41299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation