An optimal algorithm for approximate nearest neighbor searching
Sunil Arya,David M. Mount,Nathan S. Netanyahu,Ruth Silverman,Angela Y. Wu +4 more
- pp 573-582
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TLDR
It is shown that it is possible to preprocess a set of data points in real D-dimensional space in O(kd) time and in additional space, so that given a query point q, the closest point of S to S to q can be reported quickly.About:
This article is published in Symposium on Discrete Algorithms.The article was published on 1994-01-23 and is currently open access. It has received 610 citations till now. The article focuses on the topics: Best bin first & Nearest neighbor graph.read more
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Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Content-based image retrieval at the end of the early years
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Journal ArticleDOI
An efficient k-means clustering algorithm: analysis and implementation
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Proceedings ArticleDOI
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk,Rajeev Motwani +1 more
TL;DR: In this paper, the authors present two algorithms for the approximate nearest neighbor problem in high-dimensional spaces, for data sets of size n living in R d, which require space that is only polynomial in n and d.
Proceedings Article
Similarity Search in High Dimensions via Hashing
TL;DR: Experimental results indicate that the novel scheme for approximate similarity search based on hashing scales well even for a relatively large number of dimensions, and provides experimental evidence that the method gives improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition.
References
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Journal ArticleDOI
An Algorithm for Finding Best Matches in Logarithmic Expected Time
TL;DR: An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record.
Book
The design and analysis of spatial data structures
TL;DR: The design and analysis of spatial data structures and applications for predicting stock returns and remembering and imagining palestine identity and service manual are studied.
Journal ArticleDOI
A decomposition of multidimensional point sets with applications to k-nearest-neighbors and n-body potential fields
Paul B. Callahan,S. Rao Kosaraju +1 more
TL;DR: The notion of a well-separated pair decomposition of points in d-dimensional space is defined and the resulting decomposition is applied to the efficient computation of nearest neighbors and body potential fields.
Proceedings ArticleDOI
Approximate nearest neighbor queries in fixed dimensions
Sunil Arya,David M. Mount +1 more
TL;DR: A practical variant of this algorithm is implemented, and it is shown empirically that for many point distributions this variant of the algorithm finds the nearest neighbor in moderately large dimension significantly faster than existing practical approaches.
Journal ArticleDOI
Refinements to nearest-neighbor searching ink-dimensional trees
TL;DR: This note presents a simplification and generalization of an algorithm for searchingk-dimensional trees for nearest neighbors reported by Friedmanet al [3], which can be generalized to allow a partition plane to have an arbitrary orientation, rather than insisting that it be perpendicular to a coordinate axis, as in the original algorithm.
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