Underwater mine classification with imperfect labels

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

2 Scopus citations


A new algorithm for performing classification with imperfectly labeled data is presented. The proposed approach is motivated by the insight that the average prediction of a group of sufficiently informed people is often more accurate than the prediction of any one supposed expert. This idea that the "wisdom of crowds" can outperform a single expert is implemented by drawing sets of labels as samples from a Bernoulli distribution with a specified labeling error rate. Additionally, ideas from multiple imputation are exploited to provide a principled way for determining an appropriate number of label sampling rounds to consider. The approach is demonstrated in the context of an underwater mine classification application on real synthetic aperture sonar data collected at sea, with promising results.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Number of pages5
StatePublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference2010 20th International Conference on Pattern Recognition, ICPR 2010

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Underwater mine classification with imperfect labels'. Together they form a unique fingerprint.

Cite this