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226
The SRtree: An Index Structure for HighDimensional Nearest Neighbor Queries
, 1997
"... Recently, similarity queries on feature vectors have been widely used to perform contentbased retrieval of images. To apply this technique to large databases, it is required to develop multidimensional index structures supporting nearest neighbor queries e ciently. The SStree had been proposed for ..."
Abstract

Cited by 438 (3 self)
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volume than bounding rectangles with highdimensional data and that this reduces search efficiency. To overcome this drawback, we propose a new index structure called the SRtree (Sphere/Rectangletree) which integrates bounding spheres and bounding rectangles. A region of the SRtree is specified
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 ..."
Abstract

Cited by 641 (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
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
Two Algorithms for NearestNeighbor Search in High Dimensions
, 1997
"... Representing data as points in a highdimensional space, so as to use geometric methods for indexing, is an algorithmic technique with a wide array of uses. It is central to a number of areas such as information retrieval, pattern recognition, and statistical data analysis; many of the problems aris ..."
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Cited by 196 (0 self)
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Representing data as points in a highdimensional space, so as to use geometric methods for indexing, is an algorithmic technique with a wide array of uses. It is central to a number of areas such as information retrieval, pattern recognition, and statistical data analysis; many of the problems
Fast nearest neighbor search in highdimensional space
 In ICDE
, 1998
"... Similarity search in multimedia databases requires an efficient support of nearestneighbor search on a large set of highdimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearestneighbor search are not efficient in high ..."
Abstract

Cited by 52 (1 self)
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in higher dimensions. In our new approach, we therefore precompute the result of any nearestneighbor search which corresponds to a computation of the voronoi cell of each data point. In a second step, we store the voronoi cells in an index structure efficient for highdimensional data spaces. As a result
Optimal MultiStep kNearest Neighbor Search
, 1998
"... For an increasing number of modern database applications, efficient support of similarity search becomes an important task. Along with the complexity of the objects such as images, molecules and mechanical parts, also the complexity of the similarity models increases more and more. Whereas algorith ..."
Abstract

Cited by 205 (23 self)
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algorithms that are directly based on indexes work well for simple mediumdimensional similarity distance functions, they do not meet the efficiency requirements of complex highdimensional and adaptable distance functions. The use of a multistep query processing strategy is recommended in these cases
Quality and Efficiency in High Dimensional Nearest Neighbor Search
"... Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sublinearly with the dataset size, regardless of the data and query distributi ..."
Abstract

Cited by 32 (1 self)
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Nearest neighbor (NN) search in high dimensional space is an important problem in many applications. Ideally, a practical solution (i) should be implementable in a relational database, and (ii) its query cost should grow sublinearly with the dataset size, regardless of the data and query
Indexing the Solution Space: A New Technique for Nearest Neighbor Search in HighDimensional Space
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2000
"... Similarity search in multimedia databases requires an efficient support of nearestneighbor search on a large set of highdimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearestneighbor search are not efficient in highe ..."
Abstract

Cited by 36 (1 self)
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in higher dimensions. In our new approach, we therefore precompute the result of any nearestneighbor search which corresponds to a computation of the Voronoi cell of each data point. In a second step, we store conservative approximations of the Voronoi cells in an index structure efficient for highdimensional
PAC Nearest Neighbor Queries: Approximate and Controlled Search in HighDimensional and Metric Spaces
, 2000
"... In highdimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object # can be a very expensive task, because of the poor partitioning operated by index structures  the socalled "curse of dimensionality ". This also affects approximately correct (AC) alg ..."
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In highdimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object # can be a very expensive task, because of the poor partitioning operated by index structures  the socalled "curse of dimensionality ". This also affects approximately correct (AC
SKLSH: An Efficient Index Structure for Approximate Nearest Neighbor Search
"... Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradigm in many applications. Recently, Locality Sensitive Hashing (LSH) and its variants are acknowledged as the most promising solutions to ANN search. However, stateoftheart LSH approaches suffer fr ..."
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Approximate Nearest Neighbor (ANN) search in high dimensional space has become a fundamental paradigm in many applications. Recently, Locality Sensitive Hashing (LSH) and its variants are acknowledged as the most promising solutions to ANN search. However, stateoftheart LSH approaches suffer
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