Understanding scenes on many levels

Joseph Tighe, Svetlana Lazebnik

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


This paper presents a framework for image parsing with multiple label sets. For example, we may want to simultaneously label every image region according to its basic-level object category (car, building, road, tree, etc.), superordinate category (animal, vehicle, manmade object, natural object, etc.), geometric orientation (horizontal, vertical, etc.), and material (metal, glass, wood, etc.). Some object regions may also be given part names (a car can have wheels, doors, windshield, etc.). We compute co-occurrence statistics between different label types of the same region to capture relationships such as "roads are horizontal," "cars are made of metal," "cars have wheels" but "horses have legs," and so on. By incorporating these constraints into a Markov Random Field inference framework and jointly solving for all the label sets, we are able to improve the classification accuracy for all the label sets at once, achieving a richer form of image understanding.

Original languageEnglish (US)
Title of host publication2011 International Conference on Computer Vision, ICCV 2011
Number of pages8
StatePublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Other2011 IEEE International Conference on Computer Vision, ICCV 2011

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Understanding scenes on many levels'. Together they form a unique fingerprint.

Cite this