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.


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    Title :

    Fast pose estimation with parameter-sensitive hashing


    Contributors:
    Shakhnarovich, (author) / Viola, (author) / Darrell, (author)


    Publication date :

    2003-01-01


    Size :

    1302802 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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