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Single and Multiple Point Spatial Queries Supporting Keywords for Searching Nearest Neighbors

Komal K. Chhajed, Shailaja Jadhav
2015 2015 International Conference on Computing Communication Control and Automation  
Initially, spatial queries finding nearest neighbor or range queries having conditions on only geometric properties of object points.  ...  The search engine search for nearest neighbor location from the user location which the user is requesting for, where it retrieves information with the help of SI-index (Spatial Inverted) from the database  ...  Many applications require the different execution of nearest neighbor (NN) queries constrained by some properties or some adjectives like keywords of the spatial objects.  ... 
doi:10.1109/iccubea.2015.97 fatcat:dywequxd5fewbhkr2obs2o3vfu

Efficient K-Nearest Neighbor Searches for Multiple-Face Recognition in the Classroom based on Three Levels DWT-PCA

Hadi Santoso, Agus Harjoko, Agfianto Eko
2017 International Journal of Advanced Computer Science and Applications  
tree search to speed up the process of facial classification using k-Nearest Neighbor.  ...  This research looks for the best value of k to get the right facial recognition using k-fold cross-validation. 10-fold cross-validation at level 3 DWT-PCA shows that face recognition using k-Nearest Neighbor  ...  It can be used with a k-nearest neighbor (k-NN) approach to match facial features efficiently and search for the location of the nearest neighbors.  ... 
doi:10.14569/ijacsa.2017.081115 fatcat:sz4wf4mbbrdajejzx5sngzopz4

Scalable Nearest Neighbor Algorithms for High Dimensional Data

Marius Muja, David G. Lowe
2014 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Index Terms-Nearest neighbor search, big data, approximate search, algorithm configuration Ç M. Muja is with BitLit  ...  for nearest neighbor matching.  ...  Multiple randomized k-d trees are proposed in [13] as a means to speed up approximate nearest-neighbor search.  ... 
doi:10.1109/tpami.2014.2321376 pmid:26353063 fatcat:n42wwark2fdephku4jsjwfv5ky

GGNN: Graph-based GPU Nearest Neighbor Search [article]

Fabian Groh, Lukas Ruppert, Patrick Wieschollek, Hendrik P.A. Lensch
2021 arXiv   pre-print
In this paper, we propose a novel search structure based on nearest neighbor graphs and information propagation on graphs.  ...  Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations.  ...  Query Searching for k nearest neighbors is the central operation both in answering a query as well as in building the kNN search graph.  ... 
arXiv:1912.01059v3 fatcat:zbewjskznrhexkvt2zc6vacnqy

Nearest neighbor queries with peer-to-peer data sharing in mobile environments

Wei-Shinn Ku, Roger Zimmermann
2008 Pervasive and Mobile Computing  
We illustrate how previous query results cached in the local storage of neighboring mobile users can be leveraged to either fully or partially compute and verify nearest neighbor queries at a local host  ...  We present a novel approach to support nearest neighbor queries from mobile hosts by leveraging the sharing capabilities of wireless ad-hoc networks.  ...  For any nearest neighbor n i of p 1 , if q, n i + q, p 1 ≤ p 1 , n k then n i is one of the top k-nearest neighbors of q.  ... 
doi:10.1016/j.pmcj.2008.08.001 fatcat:brtiy22ucbbtnfvfya7qkisfzi


Xiangyang Xu, Tongwei Ren, Gangshan Wu
2014 Proceedings of International Conference on Internet Multimedia Computing and Service - ICIMCS '14  
Nearest neighbor searchSearch over millions, even billions of data  Images, local features, other media objects, ...  Applications  Image retrieval, computer vision, machine learning, ... 4 Database  ...  time is significantly reduced  Parallel searching on multiple computing nodes 8 Motivation and Contribution  ...  construction  Nearest neighbor searching 11 n-dimensions Queries 1.  ... 
doi:10.1145/2632856.2632868 dblp:conf/icimcs/XuRW14 fatcat:cqdtva6a2rbdnjk5cs5h4noyri

Fast Matching of Binary Features

Marius Muja, David G. Lowe
2012 2012 Ninth Conference on Computer and Robot Vision  
For vector-based features, such as SIFT and SURF, the solution has been to use approximate nearest-neighbor search, but these existing algorithms are not suitable for binary features.  ...  In this paper we introduce a new algorithm for approximate matching of binary features, based on priority search of multiple hierarchical clustering trees.  ...  Searching for nearest neighbors using parallel hierarchical clustering trees The process of searching multiple hierarchical clustering trees in parallel is presented in Algorithm 2.  ... 
doi:10.1109/crv.2012.60 dblp:conf/crv/MujaL12 fatcat:66vkhphd2nbmbnsxqvck3wndru

Fast nearest-neighbor search algorithms based on approximation-elimination search

V. Ramasubramanian, Kuldip K. Paliwal
2000 Pattern Recognition  
In this paper, we provide an overview of fast nearest-neighbor search algorithms based on an &approxima-tion}elimination' framework under a class of elimination rules, namely, partial distance elimination  ...  Previous algorithms based on these elimination rules are reviewed in the context of approximation}elimination search.  ...  Introduction Nearest-neighbor search consists in "nding the closest point to a query point among N points in K-dimensional space.  ... 
doi:10.1016/s0031-3203(99)00134-x fatcat:262obja7szdqvkq7c5ooybz6yy

Fast protein 3D surface search

Sungchul Kim, Lee Sael, Hwanjo Yu
2013 Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication - ICUIMC '13  
Experiments show that the searching time reduced 75.41% by the fast k-nearest neighbor algorithm, 88.7% by the extended fast k-nearest neighbor algorithm, 88.84% by the fast threshold-based nearest neighbor  ...  We address this need for further speed up in protein structural search by exploiting the fast k nearest neighbor algorithms on the 3DZDs.  ...  Algorithm 2 Extended Fast K Nearest Neighbor Search (efKNN) Select k vectors as a seed set of X k X k is maintained using priority queue ρ ← dist(x k , y) 2 x k is k-th nearest neighbor in X k for all  ... 
doi:10.1145/2448556.2448629 dblp:conf/icuimc/KimSY13 fatcat:ouzhrmunurbxfjzmx7ql5vdxwm

Fast Nearest Neighbor Search in the Hamming Space [chapter]

Zhansheng Jiang, Lingxi Xie, Xiaotie Deng, Weiwei Xu, Jingdong Wang
2016 Lecture Notes in Computer Science  
However, it is still computationally expensive to linearly scan the large-scale databases for nearest neighbor (NN) search. In [15], a new approximate NN search algorithm is presented.  ...  This paper generalizes the algorithm to the Hamming space with an alternative version of k-means clustering.  ...  A lot of efforts are made in accelerating nearest neighbor search. MIH [2] is an exact nearest neighbor search algorithm.  ... 
doi:10.1007/978-3-319-27671-7_27 fatcat:qo5yqswnlbdabop27lxmm4f4pm

Image Annotation with Multiple Quantization

Qiaojin Guo, Ning Li, Yubin Yang, Gangshan Wu
2011 2011 Sixth International Conference on Image and Graphics  
This leads to the problem of nearest neighbor search, which is a hot topic of pattern recognition, information retrieval, and data compression.  ...  One of the most frequently used methods is to search annotated images with similar visual features, and keywords are transfered to new coming images.  ...  This should be caused by difference of approximate and exact nearest neighbor search strategy, kNN gets the exact nearest neighbors of test image while our method gets approximate neighbors, shown as  ... 
doi:10.1109/icig.2011.15 dblp:conf/icig/GuoLYW11 fatcat:vxehiiwjbvb2lcrpw33d6u5wjy

Fast exact k nearest neighbors search using an orthogonal search tree

Yi-Ching Liaw, Maw-Lin Leou, Chien-Min Wu
2010 Pattern Recognition  
We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a  ...  The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space.  ...  In this paper, we present a new algorithm called kMkNN (k-Means for k-Nearest Neighbors) to efficiently search exact k nearest training objects for a query object.  ... 
doi:10.1016/j.patcog.2010.01.003 pmid:22247818 pmcid:PMC3255306 fatcat:gy3pt3udcrcqpk5qemr7irpeyy

Foundations of Nearest Neighbor Queries in Euclidean Space [chapter]

Hanan Samet
2017 Encyclopedia of GIS  
more importantly, there is no need to restart the k nearest neighbor search process when k increases as we can simply resume/continue the search for the k + 1 st and additional nearest neighboring objects  ...  Depth-First k Nearest Neighbor Method The most common strategy for finding the k nearest neighbors is the depth-first method which explores the elements of the search hierarchy in a depth-first manner  ...  k-nearest neighbor algorithm.  ... 
doi:10.1007/978-3-319-17885-1_1556 fatcat:odwvk6z2mrcnpicudyedc7ffay

Brute-Force k-Nearest Neighbors Search on the GPU [chapter]

Shengren Li, Nina Amenta
2015 Lecture Notes in Computer Science  
We present a brute-force approach for finding k-nearest neighbors on the GPU for many queries in parallel. Our program takes advantage of recent advances in fundamental GPU computing primitives.  ...  For instance, we can find 1000 nearest neighbors among 1 million 64-dimensional reference points at a rate of about 435 queries per second.  ...  The k-nearest neighbors problem takes sets Q and R as input, and a constant k, and returns the k nearest neighbors (kNNs) in R for every q ∈ Q.  ... 
doi:10.1007/978-3-319-25087-8_25 fatcat:vmhc4bokpzb2bk5dlrcwmn7ef4

A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems

Wei Zhang, Xiaohui Chen, Yueqi Liu, Qian Xi
2020 IEEE Access  
The D-kNN algorithm has the following advantages: First, the concept of k-nearest neighbor boundaries is proposed and the k-nearest neighbor search within the k-nearest neighbors boundaries can effectively  ...  Third, the algorithm performs k-nearest neighbor searching efficiently by performing distributed calculations at each storage node.  ...  Then, we perform a kNN search on multiple storage nodes in parallel, and use the kNNB on each storage node to further reduce the search range of the k-nearest neighbor.  ... 
doi:10.1109/access.2020.2974764 fatcat:6qd34pfpc5cbpo4pfx5ibxymou
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