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Very large scale nearest neighbor search: ideas, strategies and challenges

Erik Gast, Ard Oerlemans, Michael S. Lew
2013 International Journal of Multimedia Information Retrieval  
Web-scale databases and big data collections are computationally challenging to analyze and search.  ...  Similarity or more precisely nearest neighbor searches are thus crucial in the analysis, indexing and utilization of these massive multimedia databases.  ...  Introduction Very large scale multimedia databases are becoming common and thus searching within them has become more important.  ... 
doi:10.1007/s13735-013-0046-4 fatcat:exmlwosrc5cyzfkllzzjbu3734

Scalable Nearest Neighbor Search based on kNN Graph [article]

Wan-Lei Zhao, Jie Yang, Cheng-Hao Deng
2017 arXiv   pre-print
Nearest neighbor search is known as a challenging issue that has been studied for several decades.  ...  In addition, a comparative study on both the compressional and traditional nearest neighbor search methods is presented.  ...  neighbor search to very large range.  ... 
arXiv:1701.08475v2 fatcat:awotobncyfhizizl64niebs2lq

A Filter-And-Refine Indexing Method For Fast Similarity Search In Millions Of Music Tracks

Dominik Schnitzer, Arthur Flexer, Gerhard Widmer
2009 Zenodo  
Especially the Kullback-Leibler divergence, as it is used in the referenced works, poses multiple challenges when developing a large scale music recommendation system: (1) the divergence is very expensive  ...  CONCLUSIONS We have described a filter-and-refine method for fast approximate music similarity search in large collections.  ... 
doi:10.5281/zenodo.1417830 fatcat:hn2bjbteanbwlgck524y4h5lx4

Fast Neighborhood Graph Search using Cartesian Concatenation [article]

Jingdong Wang, Jing Wang, Gang Zeng, Rui Gan, Shipeng Li, Baining Guo
2013 arXiv   pre-print
Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the best-first strategy.  ...  Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms state-of-the-art ANN search algorithms in terms of efficiency and accuracy.  ...  Recently, the nearest neighbor search problem attracts more attentions in computer vision because of the popularity of large scale and high-dimensional multimedia data.  ... 
arXiv:1312.3062v1 fatcat:4eprr7ybcbgbjitskfm4wgskby

Graph-based Approximate NN Search: A Revisit [article]

Hui Wang, Yong Wang, Wan-Lei Zhao
2022 arXiv   pre-print
Nearest neighbor search plays a fundamental role in many disciplines such as multimedia information retrieval, data-mining, and machine learning.  ...  The optimized NN search, when being supported by the two-stage diversified graph, outperforms all the state-of-the-art approaches on both the CPU and the GPU across all the considered large-scale datasets  ...  ACKNOWLEDGMENTS This work is supported by National Natural Science Foundation of China under grants 61572408 and 61972326, and the grants of Xiamen University 20720180074.  ... 
arXiv:2204.00824v1 fatcat:iflbd3hq5zaclmdvd5l3yujrea

Anytime K-Nearest Neighbor Search for Database Applications

Weijia Xu, Daniel P. Miranker, Rui Mao, Smriti Ramakrishnan
2008 First International Workshop on Similarity Search and Applications (sisap 2008)  
Approximate solutions to k-nearest neighbor search provide much needed speed improvement to hard nearest-neighbor problems.  ...  In support of these applications, we explored a search strategy for knearest neighbor searches with MVP-trees that greedily identifies k answers and then improves the answer set monotonically.  ...  The large k value implies a large distance between the query and the kth neighbor.  ... 
doi:10.1109/sisap.2008.11 dblp:conf/sisap/XuMMR08 fatcat:nxr4l7iv7bbupgj5gw6olk6xyu

Graph based Nearest Neighbor Search: Promises and Failures [article]

Peng-Cheng Lin, Wan-Lei Zhao
2019 arXiv   pre-print
Recently, graph based nearest neighbor search gets more and more popular on large-scale retrieval tasks.  ...  The attractiveness of this type of approaches lies in its superior performance over most of the known nearest neighbor search approaches as well as its genericness to various metrics.  ...  Recently, graph based approaches such as nearest neighbor descent (NN-Descent) [13] or hill-climbing (HC) [14] , demonstrate superior performance over other categories of approaches in many large-scale  ... 
arXiv:1904.02077v5 fatcat:gdlf26kxpje55aul2hhx5qsy3u

Visual Fashion-Product Search at SK Planet [article]

Taewan Kim, Seyeong Kim, Sangil Na, Hayoon Kim, Moonki Kim, Byoung-Ki Jeon
2017 arXiv   pre-print
We build a large-scale visual search system which finds similar product images given a fashion item.  ...  Defining similarity among arbitrary fashion-products is still remains a challenging problem, even there is no exact ground-truth.  ...  The violet rectangles denote the ground-truth nearest-neighbors corresponding queries. possible to scaling up in the retrieval.  ... 
arXiv:1609.07859v6 fatcat:4gzvovotdzgu3nwanyzarqmthq

EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph [article]

Cong Fu, Deng Cai
2016 arXiv   pre-print
To the best of our knowledge, EFANNA is the fastest algorithm so far both on approximate nearest neighbor graph construction and approximate nearest neighbor search.  ...  Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of data mining, machine learning and computer vision.  ...  Any opinions, findings, and conclusions expressed here are those of the authors and do not necessarily reflect the views of the funding agencies.  ... 
arXiv:1609.07228v3 fatcat:2jwivsuv3fd5pc4watrvesjzda

Transactional Support for Visual Instance Search [chapter]

Herwig Lejsek, Friðrik Heiðar Ásmundsson, Björn Þór Jónsson, Laurent Amsaleg
2018 Lecture Notes in Computer Science  
A sequential scan was used to determine the 1,000 nearest neighbors of 500,000 query vectors, all coming from a very large collection of SIFTs.  ...  As far as we know, no nearest neighbor algorithm published so far is able to cope with all three requirements: scale, dynamicity and durability.  ... 
doi:10.1007/978-3-030-02224-2_6 fatcat:b3h3opoxyndwnetzcui7by2dci

Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data [article]

Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava
2020 arXiv   pre-print
Our theoretical results shed new light on the memory-accuracy tradeoff for nearest neighbor search, and our sketch, which consists entirely of short integer arrays, has a variety of attractive features  ...  We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset.  ...  Acknowledgements This work was supported by National Science Foundation IIS-1652131, BIGDATA-1838177, RI-1718478, AFOSR-YIP FA9550-18-1-0152, Amazon Research Award, and the ONR BRC grant on Randomized  ... 
arXiv:1902.06687v3 fatcat:ykl4bpkalzh4fpfkdlgidnavge

High Dimensional Similarity Search with Satellite System Graph: Efficiency, Scalability, and Unindexed Query Compatibility [article]

Cong Fu, Changxu Wang, Deng Cai
2021 arXiv   pre-print
Approximate Nearest Neighbor Search (ANNS) in high dimensional space is essential in database and information retrieval.  ...  Further, NSSG is proposed to reduce the indexing complexity of the SSG for large-scale applications.  ...  Exact nearest neighbor search is impractical in large-scale applications due to its high time cost. Thus, people turn to ANNS techniques.  ... 
arXiv:1907.06146v3 fatcat:hjjptdn5tzha3bqd7sp2rggnem

K-search: Searching for clusters

Rhonda Phillips, Bijaya Zenchenko
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Using this search strategy, the approximate locations of cluster means are found, automatically providing an estimate for the number of clusters, K.  ...  In experimental results, K-search can effectively identify the true number of well-separated Gaussian clusters and their locations in the presence of random background clutter (outliers).  ...  as K-search and G-means both located the true clusters very well).  ... 
doi:10.1109/icassp.2012.6288323 dblp:conf/icassp/PhillipsZ12 fatcat:2ua2ocpf4ff5dndpbvp3zqnj4e

Dynamicity and Durability in Scalable Visual Instance Search [article]

Herwig Lejsek, Björn ór Jónsson, Laurent Amsaleg, Fririk Heiar Ásmundsson
2019 arXiv   pre-print
This article addresses the issue of dynamicity and durability for scalable indexing of very large and rapidly growing collections of local features for instance retrieval.  ...  Systems designed for visual instance search face the major challenge of scalability: a collection of a few million images used for instance search typically creates a few billion features that must be  ...  Approximate Nearest Neighbor Algorithms Only approximate high-dimensional indexing solutions remain efficient at very large scale.  ... 
arXiv:1805.10942v2 fatcat:hdapj4544vhxxory4bvnxx5pzq

Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism [article]

Zhen Peng, Minjia Zhang, Kai Li, Ruoming Jin, Bin Ren
2022 arXiv   pre-print
Maximizing the performance of the search is essential for many tasks, especially at the large-scale and high-recall regime.  ...  Nearest Neighbor Search (NNS) has recently drawn a rapid increase of interest due to its core role in managing high-dimensional vector data in data science and AI applications.  ...  large-scale image search and information retrieval [41, 49, 59] , entity resolution [31] , and sequence matching [13] .  ... 
arXiv:2201.13007v1 fatcat:4qiod7i2affi3itzxehpagtv3y
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