Fast Pose Estimation with Parameter-Sensitive Hashing

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 in a way 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; 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 ...

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Gregory Shakhnarovich, Paul A. Viola, Trevor Darrell
• Keywords: Computer Vision, Hash Functions, ICCV 2003, Parameter Estimation Problems, Particular Estimation Problem, Particular Estimation Task, Pose Estimation