Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise

@inproceedings{Whitehill2009WhoseVS,
  title={Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise},
  author={Jacob Whitehill and Paul Ruvolo and Tingfan Wu and Jacob Bergsma and Javier R. Movellan},
  booktitle={NIPS},
  year={2009}
}
Modern machine learning-based approaches to computer vision require very large databases of hand labeled images. Some contemporary vision systems already require on the order of millions of images for training (e.g., Omron face detector [9]). New Internet-based services allow for a large number of labelers to collaborate around the world at very low cost. However, using these services brings interesting theoretical and practical challenges: (1) The labelers may have wide ranging levels of… CONTINUE READING

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