Unsupervised Segmentation Using Cluster Ensembles

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• 作者：Wei Zhang (20)
  Jie Yang (20)
  Wenjing Jia (21)
  Nikola Kasabov (22)
  Zhenhong Jia (23)
  Lei Zhou (20)
  
• 关键词：segmentation ; superpixels ; cluster ensembles ; LDAPPA ; multilabel
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8836
• 期：1
• 页码：76-84
• 全文大小：1,612 KB
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• 作者单位：Wei Zhang (20)
  Jie Yang (20)
  Wenjing Jia (21)
  Nikola Kasabov (22)
  Zhenhong Jia (23)
  Lei Zhou (20)
  
  20. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, China
  21. School of Computing and Communications, University of Technology, Sydney, Australia
  22. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand
  23. School of Information Science and Engineering, Xinjiang University, Urumqi, China
  
• ISSN：1611-3349

文摘

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.