Results 1  10
of
1,292,145
An investigation of practical approximate nearest neighbor algorithms
, 2004
"... This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate neares ..."
Abstract

Cited by 114 (4 self)
 Add to MetaCart
This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate
A Randomized Approximate Nearest Neighbors Algorithm
, 2010
"... We present a randomized algorithm for the approximate nearest neighbor problem in ddimensional Euclidean space. Given N points {xj} in R d, the algorithm attempts to find k nearest neighbors for each of xj, where k is a userspecified integer parameter. The algorithm is iterative, and its CPU time ..."
Abstract

Cited by 16 (0 self)
 Add to MetaCart
We present a randomized algorithm for the approximate nearest neighbor problem in ddimensional Euclidean space. Given N points {xj} in R d, the algorithm attempts to find k nearest neighbors for each of xj, where k is a userspecified integer parameter. The algorithm is iterative, and its CPU time
Are You Using the Right Approximate Nearest Neighbor Algorithm?
"... Many computer vision tasks such as largescale image retrieval and nearestneighbor classification perform similarity searches using Approximate Nearest Neighbor (ANN) indexes. These applications rely on the quality of ANN retrieval for success. Popular indexing methods for ANN queries include fore ..."
Abstract
 Add to MetaCart
Many computer vision tasks such as largescale image retrieval and nearestneighbor classification perform similarity searches using Approximate Nearest Neighbor (ANN) indexes. These applications rely on the quality of ANN retrieval for success. Popular indexing methods for ANN queries include
An Investigation of Practical ApproximateNearest Neighbor Algorithms
"... 1 Introduction The knearestneighbor searching problem is to find the k nearest points in a dataset X aeRD containing n points to a query point ..."
Abstract
 Add to MetaCart
1 Introduction The knearestneighbor searching problem is to find the k nearest points in a dataset X aeRD containing n points to a query point
An Investigation of Practical ApproximateNearest Neighbor Algorithms
"... 1 Introduction The knearestneighbor searching problem is to find the k nearest points in a dataset X ae RD containing n points to a query point q 2 RD, usually under the Euclidean distance.It has applications in a wide range of realworld settings, in particular pattern recognition, machine learni ..."
Abstract
 Add to MetaCart
1 Introduction The knearestneighbor searching problem is to find the k nearest points in a dataset X ae RD containing n points to a query point q 2 RD, usually under the Euclidean distance.It has applications in a wide range of realworld settings, in particular pattern recognition, machine
Approximate Nearest Neighbor Algorithms for Hausdorff Metrics via Embeddings
"... Hausdorff metrics are used in geometric settings for measuring the distance between sets of points. They ..."
Abstract

Cited by 33 (4 self)
 Add to MetaCart
Hausdorff metrics are used in geometric settings for measuring the distance between sets of points. They
Towards Optimal epsilonApproximate Nearest Neighbor Algorithms in Constant Dimensions
 J. Algorithms
, 2001
"... this paper, and presents what is to our knowledge the first application of stratified trees (van Emde Boas trees) to multidimensional problems. They demonstrate a twopart algorithm that performs # approximate nearest neighbor queries in expected O (d + log log N + log 1/#) ..."
Abstract
 Add to MetaCart
this paper, and presents what is to our knowledge the first application of stratified trees (van Emde Boas trees) to multidimensional problems. They demonstrate a twopart algorithm that performs # approximate nearest neighbor queries in expected O (d + log log N + log 1/#)
A Heterogeneous High Dimensional Approximate Nearest Neighbor Algorithm
"... We consider the problem of finding high dimensional approximate nearest neighbors. Suppose there are d independent rare features, each having its own independent statistics. A point x will have xi = 0 denote the absence of feature i, and xi = 1 its existence. Let pi,jk be the probability that xi = j ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
We consider the problem of finding high dimensional approximate nearest neighbors. Suppose there are d independent rare features, each having its own independent statistics. A point x will have xi = 0 denote the absence of feature i, and xi = 1 its existence. Let pi,jk be the probability that xi
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 ..."
Abstract

Cited by 448 (2 self)
 Add to MetaCart
system that answers the question, “What is the fastest approximate nearestneighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm
Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality
, 1998
"... The nearest neighbor problem is the following: Given a set of n points P = fp 1 ; : : : ; png in some metric space X, preprocess P so as to efficiently answer queries which require finding the point in P closest to a query point q 2 X. We focus on the particularly interesting case of the ddimens ..."
Abstract

Cited by 1017 (40 self)
 Add to MetaCart
, there has been some interest in the approximate nearest neighbors problem, which is: Find a point p 2 P that is an fflapproximate nearest neighbor of the query q in that for all p 0 2 P , d(p; q) (1 + ffl)d(p 0 ; q). We present two algorithmic results for the approximate version that significantly
Results 1  10
of
1,292,145