Superparsing: scalable nonparametric image parsing with superpixels

  • Authors:

  • Joseph Tighe;Svetlana Lazebnik

  • Affiliations:

  • Dept. of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC;Dept. of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC

  • Venue:
  • ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach requires no training, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. It works by scene-level matching with global image descriptors, followed by superpixel-level matching with local features and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art non-parametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 15,150 images and 170 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem.