Reducing correspondence ambiguity in loosely labeled training data

Kobus Barnard, Quanfu Fan

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

6 Scopus citations

Abstract

We develop an approach to reduce correspondence ambiguity in training data where data items are associated with sets of plausible labels. Our domain is images annotated with keywords where it is not known which part of the image a keyword refers to. In contrast to earlier approaches that build predictive models or classifiers despite the ambiguity, we argue that that it is better to first address the correspondence ambiguity, and then build more complex models from the improved training data. This addresses difficulties of fitting complex models in the face of ambiguity while exploiting all the constraints available from the training data. We contribute a simple and flexible formulation of the problem, and show results validated by a recently developed comprehensive evaluation data set and corresponding evaluation methodology.

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

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ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Barnard, K., & Fan, Q. (2007). Reducing correspondence ambiguity in loosely labeled training data. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270249] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383224