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Very large scale nearest neighbor search: ideas, strategies and challenges
2013
International Journal of Multimedia Information Retrieval
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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]
2017
arXiv
pre-print
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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
2009
Zenodo
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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]
2013
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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
2008
First International Workshop on Similarity Search and Applications (sisap 2008)
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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]
2019
arXiv
pre-print
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2019 pre-print arXiv:1904.02077v3 fatcat:qf7qc2giurhmdl2omzgv3cgxbeOther Versions
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]
2017
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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]
2018
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2020 pre-print arXiv:1907.06146v2 fatcat:2dfkoomsbfapzgbdxbsxzhgheuOther Versions
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
2012
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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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]
2019
arXiv
pre-print
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2018 pre-print arXiv:1805.10942v1 fatcat:npdvuc3n7ng2vaj2wptous2cqu
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]
2022
arXiv
pre-print
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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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