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124,017
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 ..."
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Cited by 442 (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
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 448 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional
When Is "Nearest Neighbor" Meaningful?
 In Int. Conf. on Database Theory
, 1999
"... . We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance ..."
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Cited by 402 (1 self)
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the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 1015 dimensions. These results should not be interpreted to mean that highdimensional indexing is never
Shape Indexing Using Approximate NearestNeighbour Search in HighDimensional Spaces
, 1997
"... Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use of highdimensional features is critical, due to the improved level of discrimination they can provide. Unfortunately, f ..."
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Cited by 306 (12 self)
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Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use of highdimensional features is critical, due to the improved level of discrimination they can provide. Unfortunately
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
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Cited by 1276 (124 self)
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edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two
A cost model for nearest neighbor search in highdimensional data space
 In Proc. ACM Symp. on Principles of Database Systems
, 1997
"... In this paper, we present a new cost model for nearest neighbor search in highdimensional data space. We first analyze different nearest neighbor algorithms, present a generalization of an algorithm which has been originally proposed for Quadtrees [13], and show that this algorithm is optimal. Then ..."
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Cited by 207 (22 self)
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In this paper, we present a new cost model for nearest neighbor search in highdimensional data space. We first analyze different nearest neighbor algorithms, present a generalization of an algorithm which has been originally proposed for Quadtrees [13], and show that this algorithm is optimal
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 220 (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
Efficient similarity search in sequence databases
, 1994
"... We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation being that, for most sequences of practical interest, only the first few frequencies are strong. Anot ..."
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Cited by 505 (21 self)
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. Another important observation is Parseval's theorem, which specifies that the Fourier transform preserves the Euclidean distance in the time or frequency domain. Having thus mapped sequences to a lowerdimensionality space by using only the first few Fourier coe cients, we use Rtrees to index
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 622 (13 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
Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces
, 1993
"... We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are highdim ..."
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Cited by 356 (5 self)
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We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation is very high. Also relevant are highdimensional
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