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A Novel Topic-Level Random Walk Framework for Scene Image Co-segmentation

  • Zehuan Yuan
  • Tong Lu
  • Palaiahnakote Shivakumara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

Image co-segmentation is popular with its ability to detour supervisory data by exploiting the common information in multiple images. In this paper, we aim at a more challenging branch called scene image co-segmentation, which jointly segments multiple images captured from the same scene into regions corresponding to their respective classes. We first put forward a novel representation named Visual Relation Network (VRN) to organize multiple segments, and then search for meaningful segments for every image through voting on the network. Scalable topic-level random walk is then used to solve the voting problem. Experiments on the benchmark MSRC-v2, the more difficult LabelMe and SUN datasets show the superiority over the state-of-the-art methods.

Keywords

Image co-segmentation voting random walk link analysis 

Supplementary material

978-3-319-10590-1_45_MOESM1_ESM.pdf (90 kb)
Electronic Supplementary Material (PDF 91 KB)

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zehuan Yuan
    • 1
  • Tong Lu
    • 1
  • Palaiahnakote Shivakumara
    • 2
  1. 1.National Key Laboratory of Software Novel TechnologyNanjing UniversityChina
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaMalaysia

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