Superparsing: Scalable nonparametric image parsing with superpixels

Joseph Tighe, Svetlana Lazebnik

Research output: Contribution to journalArticlepeer-review


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 is based on lazy learning, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. Given a test image, it first performs global scene-level matching against the training set, followed by superpixel-level matching 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 45,676 images and 232 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. Finally, we present an extension of our method to video sequences and report results on a video dataset with frames densely labeled at 1 Hz.

Original languageEnglish (US)
Pages (from-to)329-349
Number of pages21
JournalInternational Journal of Computer Vision
Issue number2
StatePublished - Jan 2013
Externally publishedYes


  • Image parsing
  • Image segmentation
  • Scene understanding

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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