Unsupervised segmentation of objects using efficient learning

Himanshu Arora, Nicolas Loeff, David A. Forsyth, Narendra Ahuja

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

Abstract

We describe an unsupervised method to segment objects detected in images using a novel variant of an interest point template, which is very efficient to train and evaluate. Once an object has been detected, our method segments an image using a Conditional Random Field (CRF) model. This model integrates image gradients, the location and scale of the object, the presence of object parts, and the tendency of these parts to have characteristic patterns of edges nearby. We enhance our method using multiple unsegmented images of objects to learn the parameters of the CRF, in an iterative conditional maximization framework. We show quantitative results on images of real scenes that demonstrate the accuracy of segmentation.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 11 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Arora, H., Loeff, N., Forsyth, D. A., & Ahuja, N. (2007). Unsupervised segmentation of objects using efficient learning. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270036] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383011