Segmentation Ensemble - A Knowledge Reuse for Model Order Selection using Case-based Reasoning

Pakaket Wattuya, Ekkawut Rojsattarat

2014

Abstract

Cluster ensemble has emerged as a powerful technique for improving robustness, stability, and accuracy of clustering solutions, however, automatic estimating the appropriate number of clusters in the final combined results remains unsolved. In this paper we present a new approach based on a case-based reasoning to handle this difficult task. The key success of our approach is a novel use of cluster ensemble in a different role from the past. Each ensemble component is viewed as an expert domain for building a case base. Having benefited from the information extracted from cluster ensemble, a case-based reasoning is able to settle efficiently the appropriate number of clusters underlying a clustering ensemble. Our approach is simple, fast and effective. Three simulations with different state-of-the-art segmentation algorithms are presented to illustrate the efficacy of the proposed approach. We extensively evaluate our approach on a large dataset in comparison with recent approaches for determining the number of regions in segmentation combination framework. Experiments demonstrate that our approach can substantially reduce computational time required by the existing methods, more importantly, without the loss of segmentation combination accuracy. This contribution makes the segmentation ensemble combination concept more feasible in real-world applications.

References

  1. Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Trans. Patt. Anal. Mach. Intel., 24:603-619.
  2. Deng, Y. and Manjunath, B. (2001). Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Patt. Anal. Mach. Intel., 23(8):800-810.
  3. Dubes, R. C. (1993). Cluster analysis and related issues. Handbook of Pattern Recognition & Computer Vision, pages 3-32.
  4. Felzenszwalb, P. and Huttenlocher, D. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2):167-181.
  5. Fischer, B. and Buhmann, J. (2003). Bagging for pathbased clustering. IEEE Trans. Patt. Anal. Mach. Intel., 25(11):14111415.
  6. Fowlkes, C., Martin, D., and Malik, J. (2003). Learning affinity functions for image segmentation: Combining patch-based and gradient-based approaches. In CVPR, volume 2, pages 54-61.
  7. Franek, L., Jiang, X., and Wattuya, P. (2012). Local instability problem of image segmentation algorithms: Systematic study and an ensemble-based solution. IJPRAI, 26(5).
  8. Fred, A. L. N. and Jain, A. K. (2005). Combining multiple clusterings using evidence accumulation. IEEE Trans. Patt. Anal. Mach. Intel., 27(6):835-850.
  9. Frucci, M., Perner, P., and Sanniti, G. (2008). Case-based reasoning for image segmentation. IJPRAI, 22:1-14.
  10. Grimnes, M. and Aamodt, A. (1996). A two layer casebased reasoning architecture for medical image understanding. In European Workshop on Advances in Case-Based Reasoning, pages 164-178.
  11. Halkidi, M., Batistakis, Y., and Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17:107-145.
  12. Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Trans. Syst, Man, and Cyber, 3(11):610-630.
  13. Hardy, A. (1996). On the number of clusters. Computational Statistics and Data Analysis, 23:83-96.
  14. Jiang, Y. and Zhou, Z.-H. (2004). Som ensemble-based image segmentation. 20(3):171-178.
  15. Kolodner, J. (1993). Case-Based Reasoning. Morgan Kaufmann, San Mateo, CA.
  16. Leake, D. (1996). Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press, CA.
  17. Martin, D. R., Fowlkes, C., Tal, D., and Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, pages 416-425.
  18. Milligan, G. W. and Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50:159179.
  19. Nguyen, N. and Caruana, R. (2007). Consensus clusterings. In IEEE ICDM, pages 607 - 612.
  20. Perner, P. (1999). An architecture for a cbr image segmentation system. Journal on Engineering Applications in Artificial Intelligence, 12(6):749-759.
  21. Perner, P., Guether, T., and Perner, H. (2003). Airborne fungi identification by case-based reasoning. In Workshop on CBR in the Health Sciences.
  22. Rabinovich, R., Belongie, S., Lange, T., and Buhmann, J. M. (2006). Model order selection and cue combination for image segmentation. In CVPR, pages 1130- 1137.
  23. Rao, S., Mobahi, H., Yang, A., Sastry, S., and Ma, Y. (2009). Natural image segmentation with adaptive texture and boundary encoding. In ACCV, pages 135- 146.
  24. Rohlfing, T., Russakoff, D. B., and Jr., C. R. M. (2004). Performance-based classifier combination in atlas-based image segmentation using expectationmaximization parameter estimation. IEEE Trans. Med. Imaging, 23(8).
  25. Strehl, A. and Ghosh, J. (2002). Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Machine Learning Research, 3:583-617.
  26. Topchy, A. P., Jain, A. K., and Punch, W. F. (2005). Clustering ensembles: Models of consensus and weak partitions. IEEE Trans. Patt. Anal. Mach. Intel., 27(12):1866-1881.
  27. Vega-Pons, S. and Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. IJPRAI, 25(3):337- 372.
  28. Wattuya, P., Jiang, X., Prassni, J., and Rothaus, K. (2008). A random walker based approach to combining multiple segmentations. In ICPR, pages 1-4.
  29. Wattuya, P., Soonthornphisaj, N., and Jiang, X. (2012). Using soft case-based reasoning in model order selection for image segmentation ensemble. In JSAI.
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Paper Citation


in Harvard Style

Wattuya P. and Rojsattarat E. (2014). Segmentation Ensemble - A Knowledge Reuse for Model Order Selection using Case-based Reasoning . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 287-295. DOI: 10.5220/0004830102870295


in Bibtex Style

@conference{icpram14,
author={Pakaket Wattuya and Ekkawut Rojsattarat},
title={Segmentation Ensemble - A Knowledge Reuse for Model Order Selection using Case-based Reasoning},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={287-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004830102870295},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Segmentation Ensemble - A Knowledge Reuse for Model Order Selection using Case-based Reasoning
SN - 978-989-758-018-5
AU - Wattuya P.
AU - Rojsattarat E.
PY - 2014
SP - 287
EP - 295
DO - 10.5220/0004830102870295