Superpixel Graph Label Transfer with Learned Distance Metric
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- 作者：Stephen Gould (19)
Jiecheng Zhao (19)
Xuming He (19) (20)
Yuhang Zhang (19) (21) - 刊名：Lecture Notes in Computer Science
- 出版年：2014
- 出版时间：2014
- 年：2014
- 卷：8689
- 期：1
- 页码：632-647
- 全文大小：1,796 KB
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Jiecheng Zhao (19)
Xuming He (19) (20)
Yuhang Zhang (19) (21)
19. Research School of Computer Science, ANU, Australia
20. NICTA, Australia
21. Chalmers University of Technology, Sweden - ISSN：1611-3349
文摘
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.