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Automatic Segmentation Based on AdaBoost Learning and Graph-Cuts

  • Dongfeng Han
  • Wenhui Li
  • Xiaosuo Lu
  • Tianzhu Wang
  • Yi Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

An automatic segmentation algorithm based on AdaBoost learning and iterative Graph-Cuts are shown in this paper. In order to find the approximate location of the object, AdaBoost learning method is used to automatically find the object by the trained classifier. Some details on AdaBoost are described. Then the nodes aggregation method and the iterative Graph-Cuts method are used to model the automatic segmentation problem. Compared to previous methods based on Graph-Cuts, our method is automatic. This is a main feature of the proposed algorithm. Experiments and comparisons show the efficiency of the proposed method.

Keywords

Image Segmentation Gaussian Mixture Model Segmentation Result Automatic Segmentation Residual Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dongfeng Han
    • 1
  • Wenhui Li
    • 1
  • Xiaosuo Lu
    • 1
  • Tianzhu Wang
    • 1
  • Yi Wang
    • 1
  1. 1.College of Computer Science and Technology, Key Laboratory of SymbolComputation and Knowledge Engineering of the Ministry of Education,Jilin UniversityChangchunP.R. China

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