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Building Networks for Image Segmentation Using Particle Competition and Cooperation

  • Fabricio BreveEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10404)

Abstract

Particle competition and cooperation (PCC) is a graph-based semi-supervised learning approach. When PCC is applied to interactive image segmentation tasks, pixels are converted into network nodes, and each node is connected to its k-nearest neighbors, according to the distance between a set of features extracted from the image. Building a proper network to feed PCC is crucial to achieve good segmentation results. However, some features may be more important than others to identify the segments, depending on the characteristics of the image to be segmented. In this paper, an index to evaluate candidate networks is proposed. Thus, building the network becomes a problem of optimizing some feature weights based on the proposed index. Computer simulations are performed on some real-world images from the Microsoft GrabCut database, and the segmentation results related in this paper show the effectiveness of the proposed method.

Keywords

Particle competition and cooperation Image segmentation Complex networks 

Notes

Acknowledgment

The author would like to thank the São Paulo Research Foundation - FAPESP (grant #2016/05669-4) and the National Counsel of Technological and Scientific Development - CNPq (grant #475717/2013-9) for the financial support.

References

  1. 1.
    Artan, Y.: Interactive image segmentation using machine learning techniques. In: 2011 Canadian Conference on Computer and Robot Vision (CRV), pp. 264–269, May 2011Google Scholar
  2. 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 2010Google Scholar
  3. 3.
    Blake, A., Rother, C., Brown, M., Perez, P., Torr, P.: Interactive image segmentation using an adaptive GMMRF model. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 428–441. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24670-1_33 CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 1, pp. 105–112 (2001)Google Scholar
  5. 5.
    Breve, F., Quiles, M.G., Zhao, L.: Interactive image segmentation using particle competition and cooperation. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2015Google Scholar
  6. 6.
    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 2013Google Scholar
  7. 7.
    Breve, F., Quiles, M.G., Zhao, L.: Interactive image segmentation of non-contiguous classes using particle competition and cooperation. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9155, pp. 203–216. Springer, Cham (2015). doi: 10.1007/978-3-319-21404-7_15 CrossRefGoogle Scholar
  8. 8.
    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 2012Google Scholar
  9. 9.
    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 2012Google Scholar
  10. 10.
    Breve, F., Zhao, L.: Fuzzy community structure detection by particle competition and cooperation. Soft Comput. 17(4), 659–673 (2013). http://dx.doi.org/10.1007/s00500-012-0924-3 CrossRefGoogle Scholar
  11. 11.
    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. LNCS, vol. 6677, pp. 426–433. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21111-9_48 CrossRefGoogle Scholar
  12. 12.
    Breve, F., Zhao, L., Quiles, M., Pedrycz, W., Liu, J.: Particle competition and cooperation in networks for semi-supervised learning. IEEE Trans. Knowl. Data Eng. 24(9), 1686–1698 (2012)CrossRefGoogle Scholar
  13. 13.
    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, September 2013Google Scholar
  14. 14.
    Breve, F.A.: Query rules study on active semi-supervised learning using particle competition and cooperation. In: Anais do Encontro Nacional de Inteligncia Artificial e Computacional (ENIAC), So Carlos, pp. 134–140 (2014)Google Scholar
  15. 15.
    Breve, F.A.: Auto feature weight for interactive image segmentation using particle competition and cooperation. In: Proceedings - XI Workshop de Viso Computacional WVC 2015, pp. 164–169 (2015)Google Scholar
  16. 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 2013Google Scholar
  17. 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 2010Google Scholar
  18. 18.
    Breve, F.A., Zhao, L., Quiles, M.G.: Particle competition and cooperation for semi-supervised learning with label noise. Neurocomputing 160, 63–72 (2015)CrossRefGoogle Scholar
  19. 19.
    Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. Adaptive Computation and Machine Learning. The MIT Press, Cambridge (2006)Google Scholar
  20. 20.
    Ding, L., Yilmaz, A.: Interactive image segmentation using probabilistic hypergraphs. Pattern Recogn. 43(5), 1863–1873 (2010). http://www.sciencedirect.com/science/article/pii/S0031320309004440 CrossRefzbMATHGoogle Scholar
  21. 21.
    Ducournau, A., Bretto, A.: Random walks in directed hypergraphs and application to semi-supervised image segmentation. Comput. Vis. Image Underst. 120, 91–102 (2014). http://www.sciencedirect.com/science/article/pii/S1077314213002038 CrossRefGoogle Scholar
  22. 22.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co. Inc., Boston (1989)zbMATHGoogle Scholar
  23. 23.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2008)Google Scholar
  24. 24.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  25. 25.
    Li, J., Bioucas-Dias, J., Plaza, A.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 48(11), 4085–4098 (2010)Google Scholar
  26. 26.
    Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
  27. 27.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  28. 28.
    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 2010Google Scholar
  29. 29.
    Protiere, A., Sapiro, G.: Interactive image segmentation via adaptive weighted distances. IEEE Trans. Image Process. 16(4), 1046–1057 (2007)MathSciNetCrossRefGoogle Scholar
  30. 30.
    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 CrossRefGoogle Scholar
  31. 31.
    Shapiro, L., Stockman, G.: Computer Vision. Prentice Hall, Upper Saddle River (2001)Google Scholar
  32. 32.
    Smith, A.R.: Color gamut transform pairs. ACM Siggraph Comput. Graph. 12, 12–19 (1978). ACMCrossRefGoogle Scholar
  33. 33.
    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 2008Google Scholar
  34. 34.
    Zhu, X.: Semi-supervised learning literature survey. Technical report 1530, Computer Sciences, University of Wisconsin-Madison (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.São Paulo State University (UNESP)Rio ClaroBrazil

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