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Continuous Nearest Neighbor Search
, 2002
"... A continuous nearest neighbor query retrieves the nearest neighbor (NN) of every point on a line segment (e.g., "find all my nearest gas stations during my route from point s to point e"). The result contains a set of <point, interval> tuples, such that point is the NN of all po ..."
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Cited by 160 (10 self)
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points in the corresponding interval. Existing methods for continuous nearest neighbor search are based on the repetitive application of simple NN algorithms, which incurs significant overhead. In this paper we propose techniques that solve the problem by performing a single query for the whole
Nearest Neighbor Search Methods
, 2008
"... The motion planning problem consists of finding a valid path for a robot (movable object) from a start configuration to a goal configuration without colliding with any obstacle. Probabilistic road map (PRM) methods use randomization to construct a graph (road map) of collisionfree paths that attemp ..."
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improve the performance of path planners. The brute force method of nearest neighbor search has a complexity of O(n2).This increases the running time and hence the cost. The cost of nearestneighbor calls is one of the bottlenecks in the performance of samplingbased motion planning algorithms. Therefore
Product quantization for nearest neighbor search
, 2010
"... This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace q ..."
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Cited by 222 (31 self)
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This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace
Nearest Neighbor Search in Multidimensional Spaces
, 1999
"... The Nearest Neighbor Search problem is defined as follows: given a set P of n points, preprocess the points so as to efficiently answer queries that require finding the closest point in P to a query point q. If we are willing to settle for a point that is almost as close as the nearest neighbor, t ..."
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Cited by 11 (0 self)
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The Nearest Neighbor Search problem is defined as follows: given a set P of n points, preprocess the points so as to efficiently answer queries that require finding the closest point in P to a query point q. If we are willing to settle for a point that is almost as close as the nearest neighbor
Clusteringbased Nearest Neighbor Searching
"... Abstract—This paper proposes a Clusteringbased Nearest Neighbor Search algorithm (CNNS) for high dimensional data. Different from existing approaches that are based on rigidgrid partition to develop data access structure, CNNS creates indexing structures according to data inherent distribution, wi ..."
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Abstract—This paper proposes a Clusteringbased Nearest Neighbor Search algorithm (CNNS) for high dimensional data. Different from existing approaches that are based on rigidgrid partition to develop data access structure, CNNS creates indexing structures according to data inherent distribution
Privacy Preserving Nearest Neighbor Search
, 2006
"... Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by p ..."
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Cited by 24 (1 self)
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by providing mechanisms to mine the data while giving certain privacy guarantees. In this work we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives
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 984 (32 self)
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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
Nearest Neighbor Search for Relevance Feedback
 In Proc. CVPR
, 2003
"... We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support highlevel concept queries. This ..."
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Cited by 3 (1 self)
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We introduce the problem of repetitive nearest neighbor search in relevance feedback and propose an efficient search scheme for high dimensional feature spaces. Relevance feedback learning is a popular scheme used in content based image and video retrieval to support highlevel concept queries
Product quantization for nearest neighbor search
, 2011
"... This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace ..."
Abstract
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This paper introduces a product quantization based approach for approximate nearest neighbor search. The idea is to decomposes the space into a Cartesian product of low dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace
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