Superpixel graph label transfer with learned distance metric
Paper i proceeding, 2014

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.

Search Algorithms

Training image. Transfer labels

Approximate nearest neighbor

Semantic segmentation

Distance metrics

Feature space


Image Structures


S.C. Gould

Australian National University

J. Zhao

Australian National University

X. He

Australian National University

CSIRO Data61

Zhang Yuhang

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Australian National University

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 8689 1 632-647
9783319105895 (ISBN)


Data- och informationsvetenskap




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