| dc.creator |
Shakhnarovich, Gregory |
|
| dc.creator |
Viola, Paul |
|
| dc.creator |
Darrell, Trevor |
|
| dc.date |
2004-10-08T20:38:53Z |
|
| dc.date |
2004-10-08T20:38:53Z |
|
| dc.date |
2003-04-18 |
|
| dc.date.accessioned |
2013-10-09T02:46:33Z |
|
| dc.date.available |
2013-10-09T02:46:33Z |
|
| dc.date.issued |
2013-10-09 |
|
| dc.identifier |
AIM-2003-009 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/6715 |
|
| dc.identifier.uri |
http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
|
| dc.description |
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. |
|
| dc.format |
12 p. |
|
| dc.format |
5030222 bytes |
|
| dc.format |
6836715 bytes |
|
| dc.format |
application/postscript |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.relation |
AIM-2003-009 |
|
| dc.subject |
AI |
|
| dc.subject |
parameter estimation |
|
| dc.subject |
nearest neighbor |
|
| dc.subject |
locally weighted learning |
|
| dc.title |
Fast Pose Estimation with Parameter Sensitive Hashing |
|