• DocumentCode
    2294167
  • Title

    Graph segmentation revisited: Detailed analysis and density learning based implementation

  • Author

    Yu, Zhiding ; Au, Oscar C. ; Tang, Ketan ; Li, Jiali ; Xu, Lingfeng ; Zhang, Xingyu

  • Author_Institution
    Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon, China
  • fYear
    2010
  • fDate
    19-23 July 2010
  • Firstpage
    602
  • Lastpage
    607
  • Abstract
    In this paper we give a step-by-step detailed analysis on the performance of shortest spanning tree (SST) and its revised version, recursive SST (RSST). We further propose a novel segmentation scheme based on recursive SST in the warped domain produced by density estimation. The proposed method is robust for variant natural image input and is easy to implement. Experimental results and comparisons with other methods have illustrated the effectiveness and robustness of the proposed method.
  • Keywords
    graph theory; image segmentation; recursive estimation; trees (mathematics); density learning based implementation; graph segmentation; recursive shortest spanning tree; variant natural image input; warped domain; Construction industry; Estimation; Image edge detection; Image segmentation; Kernel; Pixel; Robustness; RSST; SST; mean shift; segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Expo (ICME), 2010 IEEE International Conference on
  • Conference_Location
    Suntec City
  • ISSN
    1945-7871
  • Print_ISBN
    978-1-4244-7491-2
  • Type

    conf

  • DOI
    10.1109/ICME.2010.5583553
  • Filename
    5583553