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Nearoptimal hashing algorithms for approximate nearest neighbor in high dimensions
, 2008
"... In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The ..."
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Cited by 459 (7 self)
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In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query
An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions
 ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1994
"... Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any po ..."
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Cited by 983 (32 self)
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positive real ffl, a data point p is a (1 + ffl)approximate nearest neighbor of q if its distance from q is within a factor of (1 + ffl) of the distance to the true nearest neighbor. We show that it is possible to preprocess a set of n points in R d in O(dn log n) time and O(dn) space, so that given a
Similarity search in high dimensions via hashing
, 1999
"... The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image dat ..."
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Cited by 642 (10 self)
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The nearest or nearneighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over highdimensional data, e.g., image
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 522 (8 self)
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inÇÐÓ�Ò We present a novel LocalitySensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate
Datadependent Hashing Based on pStable Distribution
, 2014
"... The pstable distribution is traditionally used for dataindependent hashing. In this paper, we describe how to perform datadependent hashing based on pstable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this propert ..."
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The pstable distribution is traditionally used for dataindependent hashing. In this paper, we describe how to perform datadependent hashing based on pstable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based
1Datadependent Hashing Based on pStable Distribution
"... Abstract—The pstable distribution is traditionally used for dataindependent hashing. In this paper, we describe how to perform datadependent hashing based on pstable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this ..."
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Abstract—The pstable distribution is traditionally used for dataindependent hashing. In this paper, we describe how to perform datadependent hashing based on pstable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based
NearOptimal Hashing Algorithms for Approximate Nearest Neighbor in HighDimensions*
"... Abstract We present an algorithm for the capproximate nearest neighbor problem in a ddimensional Euclidean space,achieving query time of O(dn1/c 2+o(1)) and space O(dn + n1+1/c 2+o(1)). This almost matches the lower bound for hashingbased algorithm recently obtained in [27]. We alsoobtain a spac ..."
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Abstract We present an algorithm for the capproximate nearest neighbor problem in a ddimensional Euclidean space,achieving query time of O(dn1/c 2+o(1)) and space O(dn + n1+1/c 2+o(1)). This almost matches the lower bound for hashingbased algorithm recently obtained in [27]. We alsoobtain a
Fast pose estimation with parametersensitive hashing
 In ICCV
, 2003
"... Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become pro ..."
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Cited by 249 (8 self)
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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 localitysensitive hashing, a recently developed method to find approximate neighbors in time sublinear in the number
The ventriloquist effect results from nearoptimal bimodal integration
 Curr. Biol
, 2004
"... Results for the various unimodal location discriminations for naive observer L.M. are shown in Figure 1A. The curves plot the proportion of trials in which the second stimulus was seen to the left of the first, as a function of actual physical displacement. Following standard practice, the data were ..."
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Cited by 276 (13 self)
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to depend on internal noise). For all unimodal 50125 Florence conditions, the PSE was near 0�, but � varied considerItaly ably. For visual stimuli, � was smallest (approximately
Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces
, 1998
"... We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a spaceefficient data structure that would allow us to ..."
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Cited by 215 (9 self)
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We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a spaceefficient data structure that would allow us
Results 1  10
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