Unsupervised Segmentation Using Cluster Ensembles

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


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.


segmentation superpixels cluster ensembles LDAPPA multilabel 


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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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