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 become 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 locality-sensitive hashing, a recently developed method to find approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice 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.
Fast pose estimation with parameter-sensitive hashing
2003-01-01
1302802 byte
Conference paper
Electronic Resource
English
Fast Pose Estimation with Parameter-Sensitive Hashing
British Library Conference Proceedings | 2003
|Fast 3D pose estimation for on-orbit robotics
Tema Archive | 2000
|A Fast Pose Estimation Method of Four-Rotor Aircraft
Trans Tech Publications | 2014
|