SuperParsing: Scalable nonparametric image parsing with superpixels

Joseph Tighe, Svetlana Lazebnik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 nonparametric 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.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
PublisherSpringer-Verlag
Pages352-365
Number of pages14
EditionPART 5
ISBN (Print)3642155545, 9783642155543
DOIs
StatePublished - Jan 1 2010
Externally publishedYes
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: Sep 10 2010Sep 11 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume6315 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th European Conference on Computer Vision, ECCV 2010
CountryGreece
CityHeraklion, Crete
Period9/10/109/11/10

Keywords

  • image parsing
  • image segmentation
  • scene understanding

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tighe, J., & Lazebnik, S. (2010). SuperParsing: Scalable nonparametric image parsing with superpixels. In Computer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings (PART 5 ed., pp. 352-365). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6315 LNCS, No. PART 5). Springer-Verlag. https://doi.org/10.1007/978-3-642-15555-0_26