Fast Pose Estimation with Parameter Sensitive Hashing
Author(s)
Shakhnarovich, Gregory
Viola, Paul
Darrell, Trevor
Date Issued
April 18, 2003
Series/Report no.
AIM-2003-009
Abstract
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.
Subjects
AI
parameter estimation
nearest neighbor
locally weighted learning
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