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Superpixel graph label transfer with learned distance metric

S.C. Gould ; J. Zhao ; X. He ; Yuhang Zhang (Institutionen för signaler och system, Bildanalys och datorseende)
Lecture Notes in Computer Science - 13th European Conference on Computer Vision, ECCV 2014; Zurich, Switzerland; 6-12 September 2014 (0302-9743). Vol. 8689 (2014), 1, p. 632-647.
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Algorithms, Approximate nearest neighbor, Distance metrics, Feature space, Image Structures, Search Algorithms, Semantic segmentation, Training image. Transfer labels

Denna post skapades 2015-05-06. Senast ändrad 2017-07-05.
CPL Pubid: 216583


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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)


Data- och informationsvetenskap

Chalmers infrastruktur