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Superpixel Graph Label Transfer with Learned Distance Metric

  • Stephen Gould
  • Jiecheng Zhao
  • Xuming He
  • Yuhang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

We present a fast approximate nearest neighbor algorithm for semantic segmentation. Our algorithm builds a graph over superpixels from an annotated set of training images. Edges in the graph represent approximate nearest neighbors in feature space. At test time we match superpixels from a novel image to the training images by adding the novel image to the graph. A move-making search algorithm allows us to leverage the graph and image structure for finding matches. We then transfer labels from the training images to the image under test. To promote good matches between superpixels we propose to learn a distance metric that weights the edges in our graph. Our approach is evaluated on four standard semantic segmentation datasets and achieves results comparable with the state-of-the-art.

Keywords

Feature Vector Training Image Learn Distance Graph Construction Dataset Size 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Stephen Gould
    • 1
  • Jiecheng Zhao
    • 1
  • Xuming He
    • 1
    • 2
  • Yuhang Zhang
    • 1
    • 3
  1. 1.Research School of Computer ScienceANUAustralia
  2. 2.NICTAAustralia
  3. 3.Chalmers University of TechnologySweden

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