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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6715 |
| Date | 18 April 2003 |
| Creators | Shakhnarovich, Gregory, Viola, Paul, Darrell, Trevor |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 12 p., 5030222 bytes, 6836715 bytes, application/postscript, application/pdf |
| Relation | AIM-2003-009 |
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