TY - GEN
T1 - Reducing correspondence ambiguity in loosely labeled training data
AU - Barnard, Kobus
AU - Fan, Quanfu
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=34948849778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34948849778&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2007.383224
DO - 10.1109/CVPR.2007.383224
M3 - Conference contribution
AN - SCOPUS:34948849778
SN - 1424411807
SN - 9781424411801
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
T2 - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Y2 - 17 June 2007 through 22 June 2007
ER -