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ICCV

2007

IEEE

2007

IEEE

Many computer vision tasks may be expressed as the problem of learning a mapping between image space and a parameter space. For example, in human body pose estimation, recent research has directly modelled the mapping from image features (z) to joint angles (). Fitting such models requires training data in the form of labelled (z, ) pairs, from which are learned the conditional densities p(|z). Inference is then simple: given test image features z, the conditional p(|z) is immediately computed. However large amounts of training data are required to fit the models, particularly in the case where the spaces are high dimensional. We show how the use of unlabelled data--samples from the marginal distributions p(z) and p()--may be used to improve fitting. This is valuable because it is often significantly easier to obtain unlabelled than labelled samples. We use a Gaussian process latent variable model to learn the mapping from a shared latent low-dimensional manifold to the feature and pa...

Added |
14 Oct 2009 |

Updated |
30 Oct 2009 |

Type |
Conference |

Year |
2007 |

Where |
ICCV |

Authors |
Ramanan Navaratnam, Andrew W. Fitzgibbon, Roberto Cipolla |

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