Fast Pose Estimation with Parameter Sensitive Hashing
Data(s) |
08/10/2004
08/10/2004
18/04/2003
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Resumo |
Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and high-dimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly becme prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. Our algorithm extends a recently developed method for locality-sensitive hashing, which finds approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice of hash functions; we show how to find the set of hash functions that are optimally relevant to a particular estimation problem. Experiments demonstrate that the resulting algorithm, which we call Parameter-Sensitive Hashing, can rapidly and accurately estimate the articulated pose of human figures from a large database of example images. |
Formato |
12 p. 5030222 bytes 6836715 bytes application/postscript application/pdf |
Identificador |
AIM-2003-009 |
Idioma(s) |
en_US |
Relação |
AIM-2003-009 |
Palavras-Chave | #AI #parameter estimation #nearest neighbor #locally weighted learning |