Unsupervised Segmentation Using Cluster Ensembles

  • Wei Zhang
  • Jie Yang
  • Wenjing Jia
  • Nikola Kasabov
  • Zhenhong Jia
  • Lei Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8836)

Abstract

We propose a novel framework for automatic image segmentation. In this approach, a mixture of several over-segmentation methods are used to produce superpixels and then aggregation is achieved using a cluster ensemble method. Generated by different existing segmentation algorithms, superpixels can describe the manifold patterns of a natural image such as color space, smoothness and texture. We use them as the initial superpixels. Grouping cues which affect the performance of segmentation can also be captured. After the over-segmentation, the simultaneous collection of superpixels is expected to achieve synergistic effects and ensure the accuracy of the segmentation. For this purpose, cluster ensemble methods are used to process the initial segmentation results and produce the final result. Our method achieves significantly better performance on the Berkeley Segmentation Database compared to state-of-the-art techniques.

Keywords

segmentation superpixels cluster ensembles LDAPPA multilabel 

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References

  1. 1.
    Zhou, L., Gong, C., Li, Y., Qiao, Y., Yang, J., Kasabov, N.: Salient Object Segmentation Based on Automatic Labeling. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part III. LNCS, vol. 8228, pp. 584–591. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Kim, T.H., Lee, K.M., Lee, S.U.: Learning full pairwise affinities for spectral segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(7), 1690–1703 (2013)CrossRefGoogle Scholar
  3. 3.
    Ren, Z., Shakhnarovich, G.: Image segmentation by cascaded region agglomeration. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2011–2018. IEEE (June 2013)Google Scholar
  4. 4.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  5. 5.
    Hoiem, D., Efros, A.A., Hebert, M.: Recovering occlusion boundaries from an image. International Journal of Computer Vision 91(3), 328–346 (2011)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  7. 7.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  8. 8.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(5), 898–916 (2011)CrossRefGoogle Scholar
  9. 9.
    Fern, X.Z., Brodley, C.E.: Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 36. ACM (July 2004)Google Scholar
  10. 10.
    Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 10–17. IEEE (October 2003)Google Scholar
  11. 11.
    Van den Bergh, M., Boix, X., Roig, G., de Capitani, B., Van Gool, L.: SEEDS: Superpixels extracted via energy-driven sampling. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 13–26. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 929–944 (2007)CrossRefGoogle Scholar
  13. 13.
    Meilǎ, M.: Comparing clusterings: an axiomatic view. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 577–584. ACM (August 2005)Google Scholar
  14. 14.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 416–423. IEEE (2001)Google Scholar
  15. 15.
    Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet another survey on image segmentation: Region and boundary information integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Maire, M., Arbeláez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (June 2008)Google Scholar
  17. 17.
    Topchy, A.P., Jain, A.K., Punch, W.F.: A Mixture Model for Clustering Ensembles. In: SDM (April 2004)Google Scholar
  18. 18.
    Hammouda, K., Jernigan, E.: Texture segmentation using gabor filters. Center for Intelligent Machines, McGill University, Canada (2000)Google Scholar
  19. 19.
    Strehl, A., Ghosh, J.: Cluster ensembles—a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3, 583–617 (2003)MathSciNetMATHGoogle Scholar
  20. 20.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wei Zhang
    • 1
  • Jie Yang
    • 1
  • Wenjing Jia
    • 2
  • Nikola Kasabov
    • 3
  • Zhenhong Jia
    • 4
  • Lei Zhou
    • 1
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityChina
  2. 2.School of Computing and CommunicationsUniversity of Technology, SydneyAustralia
  3. 3.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  4. 4.School of Information Science and EngineeringXinjiang UniversityUrumqiChina

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