Skip to main content
SpringerLink
Log in

Interactive Image Segmentation of Non-contiguous Classes Using Particle Competition and Cooperation

  • Conference paper
  • First Online:
Computational Science and Its Applications -- ICCSA 2015 (ICCSA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9155))

Included in the following conference series:

Abstract

Semi-supervised learning methods employ both labeled and unlabeled data in their training process. Therefore, they are commonly applied to interactive image processing tasks, where a human specialist may label a few pixels from the image and the algorithm would automatically propagate them to the remaining pixels, classifying the entire image. The particle competition and cooperation model is a recently proposed graph-based model, which was developed to perform semi-supervised classification. It employs teams of particles walking in a undirected and unweighed graph in order to classify data items corresponding to graph nodes. Each team represents a class problem, they try to dominate the unlabeled nodes in their neighborhood, at the same time that they try to avoid invasion from other teams. In this paper, the particle competition and cooperation model is applied to the task of interactive image segmentation. Image pixels are converted to graph nodes. Nodes are connected if they represent pixels with visual similarities. Labeled pixels generate particles that propagate their labels to the unlabeled pixels. Computer simulations are performed on some real-world images to show the effectiveness of the proposed approach. Images are correctly segmented in regions of interest, including non-contiguous regions.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Artan, Y.: Interactive image segmentation using machine learning techniques. In: 2011 Canadian Conference on Computer and Robot Vision (CRV), pp. 264–269 (May 2011)

    Google Scholar 

  2. Artan, Y., Yetik, I.: Improved random walker algorithm for image segmentation. In: 2010 IEEE Southwest Symposium on Image Analysis Interpretation (SSIAI), pp. 89–92 (May 2010)

    Google Scholar 

  3. Belkin, M., Matveeva, I., Niyogi, P.: Regularization and semi-supervised learning on large graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624–638. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  4. Belkin, M., Niyogi, P., Sindhwani, V.: On manifold regularization. In: Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, AISTAT 2005, pp. 17–24. Society for Artificial Intelligence and Statistics, New Jersey (2005)

    Google Scholar 

  5. Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive Image Segmentation Using an Adaptive GMMRF Model. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004). http://dx.doi.org/10.1007/978-3-540-24670-1_33

  6. Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 19–26. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  7. Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary amp; region segmentation of objects in n-d images. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 105–112 (2001)

    Google Scholar 

  8. Breve, F.: Active semi-supervised learning using particle competition and cooperation in networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (August 2013)

    Google Scholar 

  9. Breve, F., Zhao, L.: Particle competition and cooperation in networks for semi-supervised learning with concept drift. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (June 2012)

    Google Scholar 

  10. Breve, F., Zhao, L.: Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning. In: 2012 Brazilian Symposium on Neural Networks (SBRN), pp. 79–84 (October 2012)

    Google Scholar 

  11. Breve, F., Zhao, L.: Fuzzy community structure detection by particle competition and cooperation. Soft Computing 17(4), 659–673 (2013). http://dx.doi.org/10.1007/s00500-012-0924-3

  12. Breve, F., Zhao, L., Quiles, M., Pedrycz, W., Liu, J.: Particle competition and cooperation for uncovering network overlap community structure. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part III. LNCS, vol. 6677, pp. 426–433. Springer, Heidelberg (2011). http://dx.doi.org/10.1007/978-3-642-21111-9_48

  13. Breve, F., Zhao, L., Quiles, M., Pedrycz, W., Liu, J.: Particle competition and cooperation in networks for semi-supervised learning. IEEE Transactions on Knowledge and Data Engineering 24(9), 1686–1698 (2012)

    Google Scholar 

  14. Breve, F.A.: Combined active and semi-supervised learning using particle walking temporal dynamics. In: 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), pp. 15–20 (Septemeber 2013)

    Google Scholar 

  15. Breve, F.A.: Query rules study on active semi-supervised learning using particle competition and cooperation. In: Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), pp. 134–140. São Carlos (2014)

    Google Scholar 

  16. Breve, F.A., Zhao, L.: Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data. In: 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), pp. 335–340 (September 2013)

    Google Scholar 

  17. Breve, F.A., Zhao, L., Quiles, M.G.: Semi-supervised learning from imperfect data through particle cooperation and competition. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (July 2010)

    Google Scholar 

  18. Breve, F.A., Zhao, L., Quiles, M.G.: Particle competition and cooperation for semi-supervised learning with label noise. Neurocomputing (2015) (article in Press)

    Google Scholar 

  19. Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2006)

    Google Scholar 

  20. Ding, L., Yilmaz, A.: Interactive image segmentation using probabilistic hypergraphs. Pattern Recognition 43(5), 1863–1873 (2010). http://www.sciencedirect.com/science/article/pii/S0031320309004440

  21. Ducournau, A., Bretto, A.: Random walks in directed hypergraphs and application to semi-supervised image segmentation. Computer Vision and Image Understanding 120, 91–102 (2014). http://www.sciencedirect.com/science/article/pii/S1077314213002038

  22. Friedman, J.H., Bentley, J.L., Finkel, R.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977). http://doi.acm.org/10.1145/355744.355745

  23. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2008)

    Google Scholar 

  24. Grady, L.: Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)

    Article  Google Scholar 

  25. Joachims, T.: Transductive learning via spectral graph partitioning. In: Proceedings of International Conference on Machine Learning, pp. 290–297. AAAI Press (2003)

    Google Scholar 

  26. Li, J., Bioucas-Dias, J., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote Sensing 48(11), 4085–4098 (2010)

    Google Scholar 

  27. Paiva, A., Tasdizen, T.: Fast semi-supervised image segmentation by novelty selection. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1054–1057 (March 2010)

    Google Scholar 

  28. Protiere, A., Sapiro, G.: Interactive image segmentation via adaptive weighted distances. IEEE Transactions on Image Processing 16(4), 1046–1057 (2007)

    Article  MathSciNet  Google Scholar 

  29. Rother, C., Kolmogorov, V., Blake, A.: grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004). http://doi.acm.org/10.1145/1015706.1015720

  30. Shapiro, L., Stockman, G.: Computer Vision. Prentice Hall (2001)

    Google Scholar 

  31. Smith, A.R.: Color gamut transform pairs. In: ACM Siggraph Computer Graphics, vol. 12, pp. 12–19. ACM (1978)

    Google Scholar 

  32. Xu, J., Chen, X., Huang, X.: Interactive image segmentation by semi-supervised learning ensemble. In: International Symposium on Knowledge Acquisition and Modeling, KAM 2008, pp. 645–648 (December 2008)

    Google Scholar 

  33. Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems, vol. 16, pp. 321–328. MIT Press (2004). http://www.kyb.tuebingen.mpg.de/bs/people/weston/localglobal.pdf

  34. Zhu, X.: Semi-supervised learning literature survey. Tech. Rep. 1530, Computer Sciences, University of Wisconsin-Madison (2005)

    Google Scholar 

  35. Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the Twentieth International Conference on Machine Learning, pp. 912–919 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. São Paulo State University (UNESP), Rio Claro, SP, Brazil

    Fabricio Breve

  2. Federal University of São Paulo (Unifesp), Rio Claro, SP, Brazil

    Marcos G. Quiles

  3. University of São Paulo (USP), Rio Claro, SP, Brazil

    Liang Zhao

Authors
  1. Fabricio Breve
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcos G. Quiles
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabricio Breve .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Canaanland, Nigeria

    Sanjay Misra

  4. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  5. University of Minho, Braga, Portugal

    Ana Maria Alves Coutinho Rocha

  6. Polytechnic University, Bari, Italy

    Carmelo Torre

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Breve, F., Quiles, M.G., Zhao, L. (2015). Interactive Image Segmentation of Non-contiguous Classes Using Particle Competition and Cooperation. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9155. Springer, Cham. https://doi.org/10.1007/978-3-319-21404-7_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21404-7_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21403-0

  • Online ISBN: 978-3-319-21404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation