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A fast nearestneighbor algorithm based on a principal axis search tree
 IEEE T. Pattern. Anal
, 2001
"... AbstractÐA new fast nearestneighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum variance. The search algorithm efficiently uses a depthfirst search and a new el ..."
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Cited by 46 (0 self)
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AbstractÐA new fast nearestneighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum variance. The search algorithm efficiently uses a depthfirst search and a new
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 455 (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 high
Distinctive Image Features from ScaleInvariant Keypoints
, 2003
"... This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substa ..."
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Cited by 8955 (21 self)
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describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearestneighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object
Nearest neighbor queries.
 ACM SIGMOD Record,
, 1995
"... Abstract A frequently encountered type of query in Geographic Information Systems is to nd the k nearest neighbor objects to a given point in space. Processing such queries requires substantially di erent search algorithms than those for location or range queries. In this paper we present a n e cie ..."
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Cited by 592 (1 self)
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Abstract A frequently encountered type of query in Geographic Information Systems is to nd the k nearest neighbor objects to a given point in space. Processing such queries requires substantially di erent search algorithms than those for location or range queries. In this paper we present a n e
Fast kNN Classification Rule Using Metrics on SpaceFilling Curves
"... A fast nearest neighbor algorithm for pattern classification is proposed and tested on real data. The patterns (points in ddimensional Euclidean space) are sorted along a spacefilling curve. This way the multidimensional problem is compressed to the simplest case of the nearest neighbor search in ..."
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A fast nearest neighbor algorithm for pattern classification is proposed and tested on real data. The patterns (points in ddimensional Euclidean space) are sorted along a spacefilling curve. This way the multidimensional problem is compressed to the simplest case of the nearest neighbor search
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
A Hybrid NearestNeighbor and NearestHyperrectangle Algorithm
 in the Proceedings of the 7th European Conference on Machine Learning
, 1994
"... . Algorithms based on Nested Generalized Exemplar (NGE) theory [10] classify new data points by computing their distance to the nearest "generalized exemplar" (i.e. an axisparallel multidimensional rectangle). An improved version of NGE, called BNGE, was previously shown to perform compar ..."
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Cited by 39 (1 self)
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comparably to the Nearest Neighbor algorithm. Advantages of the NGE approach include compact representation of the training data and fast training and classification. A hybrid method that combines BNGE and the kNearest Neighbor algorithm, called KBNGE, is introduced for improved classification accuracy
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 457 (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
Fastmap: A fast algorithm for indexing, datamining and visualization of traditional and multimedia datasets
, 1995
"... A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in kd space, using k featureextraction functions, provided by a domain expert [Jag91]. Thus, we can subsequently use highly finetuned spatial access methods (SAMs), to answer several ..."
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Cited by 502 (22 self)
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types of queries, including the `Query By Example' type (which translates to a range query); the `all pairs' query (which translates to a spatial join [BKSS94]); the nearestneighbor or bestmatch query, etc. However, designing feature extraction functions can be hard. It is relatively
Cover trees for nearest neighbor
 In Proceedings of the 23rd international conference on Machine learning
, 2006
"... ABSTRACT. We present a tree data structure for fast nearest neighbor operations in generalpoint metric spaces. The data structure requires space regardless of the metric’s structure. If the point set has an expansion constant � in the sense of Karger and Ruhl [KR02], the data structure can be const ..."
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Cited by 218 (0 self)
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ABSTRACT. We present a tree data structure for fast nearest neighbor operations in generalpoint metric spaces. The data structure requires space regardless of the metric’s structure. If the point set has an expansion constant � in the sense of Karger and Ruhl [KR02], the data structure can
Results 1  10
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