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Distancebased indexing for highdimensional metric spaces
 In Proc. ACM SIGMOD International Conference on Management of Data
, 1997
"... In many database applications, one of the common queries is to find approximate matches to a given query item from a collection of data items. For example, given an image database, one may want to retrieve all images that are similar to a given query image. Distance based index structures are propos ..."
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Cited by 133 (3 self)
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are proposed for applications where the data domain is high dimensional, or the distance function used to compute distances between data objects is nonEuclidean. In this paper, we introduce a distance based index structure called multivantage point (mvp) tree for similarity queries on highdimensional metric
An Effective Clustering Algorithm to Index High Dimensional Metric Spaces
"... A metric space consists of a collection of objects and a distance function defined among them, which satisfies the triangular inequality. The goal is to preprocess the set so that, given a set of objects and a query, retrieve those objects close enough to the query. The number of distances computed ..."
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Cited by 19 (7 self)
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to achieve this goal is the complexity measure. The problem is very difficult in the socalled highdimensional metric spaces, where the histogram of distances has a large mean and a small variance. A recent survey on methods to index metric spaces has shown that the socalled clustering algorithms
A parallel similarity search in high dimensional metric space using Mtree
 Advanced Environments, Tools, and Applications for Cluster Computing
, 2002
"... Abstract. In this study, parallel implementation of Mtree to index high dimensional metric space has been elaborated and an optimal declustering technique has been proposed. First, we have defined the optimal declustering and developed an algorithm based on this definition. Proposed declustering al ..."
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Cited by 2 (0 self)
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Abstract. In this study, parallel implementation of Mtree to index high dimensional metric space has been elaborated and an optimal declustering technique has been proposed. First, we have defined the optimal declustering and developed an algorithm based on this definition. Proposed declustering
Cluster Splitting Based High Dimensional Metric Space Index B+Tree
"... Abstract: In order to improve the query efficiency, Kmeans cluster approach is often used to estimate the data distribution in the context of high dimensional metric space index. But in previous work, the parameters of clustering are usually selected according to some heuristic manner. This paper ..."
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Abstract: In order to improve the query efficiency, Kmeans cluster approach is often used to estimate the data distribution in the context of high dimensional metric space index. But in previous work, the parameters of clustering are usually selected according to some heuristic manner. This paper
Using the Distance Distribution for Approximate Similarity Queries in HighDimensional Metric Spaces
 IN PROC. OF THE 10TH INT’L WORKSHOP ON DATABASE & EXPERT SYSTEMS APPLICATIONS
, 1999
"... We investigate the problem of approximate similarity (nearest neighbor) search in highdimensional metric spaces, and describe how the distance distribution of the query object can be exploited so as to provide probabilistic guarantees on the quality of the result. This leads to a new paradigm for s ..."
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Cited by 2 (0 self)
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We investigate the problem of approximate similarity (nearest neighbor) search in highdimensional metric spaces, and describe how the distance distribution of the query object can be exploited so as to provide probabilistic guarantees on the quality of the result. This leads to a new paradigm
BM+Tree: A HyperplaneBased Index Method for HighDimensional Metric Spaces
 IN DASFAA
, 2005
"... In this paper, we propose a novel highdimensional index method, the BM+tree, to support efficient processing of similarity search queries in highdimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a highdimensional space by using a rotary bin ..."
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Cited by 3 (0 self)
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In this paper, we propose a novel highdimensional index method, the BM+tree, to support efficient processing of similarity search queries in highdimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a highdimensional space by using a rotary
PAC Nearest Neighbor Queries: Using the Distance Distribution for Searching in HighDimensional Metric Spaces
"... In this paper we introduce a new paradigm for similarity search, called PACNN (p obably approximately correct nearest neighbor ) queries, aiming to break the "dimensionality curse" which inhibits current approaches to be applied in highdimensional spaces. PACNN queries return, with pro ..."
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In this paper we introduce a new paradigm for similarity search, called PACNN (p obably approximately correct nearest neighbor ) queries, aiming to break the "dimensionality curse" which inhibits current approaches to be applied in highdimensional spaces. PACNN queries return
Searching in metric spaces
, 2001
"... The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather gen ..."
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Cited by 432 (38 self)
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general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without crossknowledge. Because of this, the same ideas have been reconceived several times, and very different
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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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 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 nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)
Results 1  10
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