• DocumentCode
    2115327
  • Title

    Can similar scenes help surface layout estimation?

  • Author

    Divvala, Santosh K. ; Efros, Alexei A. ; Hebert, Martial

  • Author_Institution
    Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA
  • fYear
    2008
  • fDate
    23-28 June 2008
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    We describe a preliminary investigation of utilising large amounts of unlabelled image data to help in the estimation of rough scene layout. We take the single-view geometry estimation system of Hoiem et al (2207) as the baseline and see if it is possible to improve its performance by considering a set of similar scenes gathered from the Web. The two complimentary approaches being considered are 1) improving surface classification by using average geometry estimated from the matches, and 2) improving surface segmentation by injecting segments generated from the average of the matched images. The system is evaluated using the labelled 300-image dataset of Hoiem et al. and shows promising results.
  • Keywords
    image classification; image matching; image segmentation; rough scene layout estimation; surface classification; surface layout estimation; surface segmentation; Computational geometry; Humans; Image segmentation; Internet; Layout; Robots; Rough surfaces; Supervised learning; Surface roughness; Surface texture;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    2160-7508
  • Print_ISBN
    978-1-4244-2339-2
  • Electronic_ISBN
    2160-7508
  • Type

    conf

  • DOI
    10.1109/CVPRW.2008.4562951
  • Filename
    4562951