Superpixel Graph Label Transfer with Learned Distance Metric

Stephen Gould¹, Jiecheng Zhao¹, Xuming He^{1, 2}, and Yuhang Zhang^{1, 3}

¹Research School of Computer Science, ANU, Australia

²NICTA, Australia

³Chalmers University of Technology, Sweden

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.

LNCS 8689, p. 632 ff.

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