[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Soft spectral clustering ensemble applied to image segmentation

  • Research Article
  • Published:
Frontiers of Computer Science in China Aims and scope Submit manuscript

Abstract

An unsupervised learning algorithm, named soft spectral clustering ensemble (SSCE), is proposed in this paper. Until now many proposed ensemble algorithms cannot be used on image data, even images of a mere 256 × 256 pixels are too expensive in computational cost and storage. The proposed method is suitable for performing image segmentation and can, to some degree, solve some open problems of spectral clustering (SC). In this paper, a random scaling parameter and Nyström approximation are applied to generate the individual spectral clusters for ensemble learning. We slightly modify the standard SC algorithm to aquire a soft partition and then map it via a centralized logcontrast transform to relax the constraint of probability data, the sum of which is one. All mapped data are concatenated to form the new features for each instance. Principal component analysis (PCA) is used to reduce the dimension of the new features. The final aggregated result can be achieved by clustering dimension-reduced data. Experimental results, on UCI data and different image types, show that the proposed algorithm is more efficient compared with some existing consensus functions.

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

Similar content being viewed by others

References

  1. Duda R O, Hart P E, Stork D G. Pattern classification. New York: Wiley Interscience, 2001

    MATH  Google Scholar 

  2. Dietterich T G. Machine-learning research: four current directions. AI Magazine, 1997, 18(4): 97–136

    Google Scholar 

  3. Zhou Z H, Wu J, Tang W. Ensembling neural networks: many could be better than all. Artificial Intelligence, 2002, 137(1–2): 239–263

    Article  MathSciNet  MATH  Google Scholar 

  4. Tang W, Zhou Z. Bagging-based selective clustering ensemble. Journal of Software, 2005, 34(12): 496–502

    Article  Google Scholar 

  5. Strehl A, Ghosh J. Cluster ensembles—a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 2002, 3(12): 583–617

    MathSciNet  Google Scholar 

  6. Liu X, Wang Z, Chen W, Li X. Remote sensing images hierarchical clustering using Markov random field and generalized Gaussian mixture models. Journal of Remote Sensing, 2007, 11(6): 838–844

    Google Scholar 

  7. Fan G, Xia X. A joint multicontext and multiscale approach to Bayesian image segmentation. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(12): 2680–2688

    Article  Google Scholar 

  8. Topchy A P, Law M H C, Jain A K, Fred A L. Analysis of consensus partition in cluster ensemble. In: Proceedings of the 4th IEEE International Conference on Data Mining. 2004, 225–232

  9. Munkres J. Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 1957, 5(1): 32–38

    Article  MathSciNet  MATH  Google Scholar 

  10. MacQueen J B. Some methods for classification and analysis of multivariate observation. In: Proceeding of the 5th Berkeley Symp. on Mathematical Statistics and Probability. Berkeley: University of California Press, 1967, 281–297

    Google Scholar 

  11. Ding C H Q, He X, Zha H, Gu M, Simon H D. A min-max cut algorithm for graph partitioning and data clustering. In: Proceedings of IEEE International Conference on Data Mining. 2001, 107–114

  12. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888–905

    Article  Google Scholar 

  13. Ng A, Jordan M, Weiss Y. On spectral clustering: analysis and an algorithm. Advance of Neural Information Processing System. Cambridge: MIT Press, 2002, 849–856

    Google Scholar 

  14. Wang S, Siskind J M. Image segmentation with ratio cut. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6): 675–690

    Article  Google Scholar 

  15. Bach F R, Jordan M I. Blind one-microphone speech separation: a spectral learning approach. In: Proceedings of Advance of Neural Information Processing System (NIPS). Cambridge: MIT Press, 2005

    Google Scholar 

  16. Odobez J M, Gatica-Perez D, Guillemot M. Video shot clustering using spectral methods. In: Proceedings of International Workshop on Content-based Multimedia Indexing. Rennes, 2003, 94–102

  17. Zhang X, Jiao L, Liu F, Bo L, Gong M. Spectral clustering ensemble applied to SAR image segmentation. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7): 2126–2136

    Article  Google Scholar 

  18. Dhillon I S. Co-clustering documents and words using bipartite spectral graph parititioning. In: Proceedings of Knowledge Discovery and Data Mine. 2001, 269–274

  19. Topchy A, Jain A K, Punch W. Clustering ensembles: models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(12): 1866–1881

    Article  Google Scholar 

  20. Topchy A, Jain A K, Punch W. Combining multiple weak clusterings. In: Proceedings of IEEE International Conference on Data Mining. Melbourne, 2003, 331–338

  21. Fred A L N, Jain A K. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(6): 835–850

    Article  Google Scholar 

  22. Fred A L N. Data clustering using evidence accumulation. In: Proceeding of the 16th International Conference on Pattern Recognition. Quebec, 2002, 276–280

  23. Fred A L N. Finding consistent clusters in data partitions. In: Proceeding of the 3d InternationalWorkshop on Multiple Classifier System. Roli J K F, ed. LNCS 2364, 2001, 309–318

  24. Fern X Z, Brodley C E. Random projection for high dimensional data clustering: a cluster ensemble approach. In: Proceedings of the 20th International Conference on Machine Learning (ICML). Washington DC, 2003, 186–193

  25. Fischer B, Buhmann J M. Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(4): 513–518

    Article  Google Scholar 

  26. Minaei-Bidgoli B, Topchy A, Punch W F. Ensembles of partitions via data resampling. In: Proceedings of International Conference on Information Technology, ITCC. LasVegas, 2004, 188–192

    Google Scholar 

  27. Fred A L N. Finding consistent clusters in data partitions. In: Proceedings of the 3d International Workshop on Multiple Classifier System. Roli J K F (ed.), LNCS 2364, 2001, 309–318

  28. Wang X, Yang C Y, Zhou J. Clustering aggregation by probability accumulation. Pattern Recognition, 2009, 42(5): 668–675

    Article  MATH  Google Scholar 

  29. Karypis G, Kumar V. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 1995, 20(1): 359–392

    Article  MathSciNet  Google Scholar 

  30. Karypis G, Aggarwal R, Kumar V, Shekhar S. Multilevel hypergraph partitioning: application in VLSI domain. In: Proceedings of ACM/IEEE Design Automation Conference, 1997, 526–529

  31. Fern X Z, Brodley C E. Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of the 21st International Conference on Machine Learning (ICML). Canada, 2004

  32. Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series A (General), 1977, 39(1): 1–38

    MathSciNet  MATH  Google Scholar 

  33. Punera K, Ghosh J. Soft cluster ensembles. In: Oliveira J V, Pedrycz W, eds. Advances in Fuzzy Clustering and its Applications. Wiley, 2007, 69–90

  34. Gao Y, Gu S, Xia L, et al. Fuzzy clustering ensemble based on mutual information. In: Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, 2006, 476–481

  35. Yu Z, Deng Z, Wong H, et al. Fuzzy cluster ensemble and its application on 3d head model classification. In: Proceedings of International Joint Conference on Neural Networks (IJCNN). 2008, 569–576

  36. Zhai S, Luo B, Guo Y. Fuzzy clustering ensemble based on dual boosting. In: Proceedings of 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). 2007

  37. Fowlkes C, Belongie S, Chung F, Malik J. Spectral grouping using the Nyström method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 214–225

    Article  Google Scholar 

  38. Zhang X. Classification and Segmentation of SAR Images Based on Selective Feature Fusion and Ensemble Learning. Xi’an: Xidian University, 2006

    Google Scholar 

  39. Barthelemy J P, Leclerc B. The median procedure for partitions. In: Cox I J, Hansen P, Julesz B, eds. Partitioning Data Sets. American Mathematical Society, Providence, 1995, 3–34

    Google Scholar 

  40. Aitchison J. The statistical analysis of compositional data. Journal of the Royal Statistical Society, Series B. Methodological, 1982, 44(2): 139–177

    MathSciNet  MATH  Google Scholar 

  41. Billheimer D, Guttorp P, Fagan W F. Statistical analysis and interpretation of discrete compositional data. National Center for Statistics and the Environment (NRCSE) Technical Report NRCSE-TRS, 11, 1998

  42. Blake C L, Merz C J. UCI repository of machine learning databases.1998, available from: http://www.ics.uci.edu/mlearn/MLRepository

  43. Hansen L, Salamon P. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(10): 993–1001

    Article  Google Scholar 

  44. Melville P, Mooney R J. Constructing diverse classifier ensembles using artificial training examples. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence. 2003, 505–510

  45. Opitz D, Maclin R. Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research, 1999, 11(8): 169–198

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, 333002, China

    Jianhua Jia & Bingxiang Liu

  2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, China

    Jianhua Jia & Licheng Jiao

Authors
  1. Jianhua Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bingxiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Licheng Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Jia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jia, J., Liu, B. & Jiao, L. Soft spectral clustering ensemble applied to image segmentation. Front. Comput. Sci. China 5, 66–78 (2011). https://doi.org/10.1007/s11704-010-0161-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-010-0161-9

Keywords

Search

Navigation