Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6715
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dc.creatorShakhnarovich, Gregory-
dc.creatorViola, Paul-
dc.creatorDarrell, Trevor-
dc.date2004-10-08T20:38:53Z-
dc.date2004-10-08T20:38:53Z-
dc.date2003-04-18-
dc.date.accessioned2013-10-09T02:46:33Z-
dc.date.available2013-10-09T02:46:33Z-
dc.date.issued2013-10-09-
dc.identifierAIM-2003-009-
dc.identifierhttp://hdl.handle.net/1721.1/6715-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionExample-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.format12 p.-
dc.format5030222 bytes-
dc.format6836715 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAIM-2003-009-
dc.subjectAI-
dc.subjectparameter estimation-
dc.subjectnearest neighbor-
dc.subjectlocally weighted learning-
dc.titleFast Pose Estimation with Parameter Sensitive Hashing-
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