mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data-Reference-Cited by-同舟云学术

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mmLSH: A Practical and Efficient Technique for Processing Approximate Nearest Neighbor Queries on Multimedia Data

Published:2020 Issue: Volume: Page:47-61
ISSN:0302-9743
Container-title:Similarity Search and Applications
language:
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Author:

Jafari Omid^ORCID,Nagarkar Parth^ORCID,Montaño Jonathan^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-60936-8_4

Reference23 articles.

1. Bartolini, I., Ciaccia, P., Patella, M.: Query processing issues in region-based image databases. Knowl. Inf. Syst. 25, 389–420 (2010). https://doi.org/10.1007/s10115-009-0257-4

2. Arora, A., Sinha, S., Kumar, P., Bhattacharya, A.: Hd-index: pushing the scalability-accuracy boundary for approximate kNN search. In: VLDB (2018)

3. Caltech dataset. http://www.vision.caltech.edu/Image_Datasets/Caltech256

4. Lecture Notes in Computer Science;T Christiani,2019

5. Corel dataset. http://www.ci.gxnu.edu.cn/cbir/Dataset.aspx

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Manu;Proceedings of the VLDB Endowment;2022-08

2. P-QALSH: Parallelizing Query Aware Locality-Sensitive Hashing for Big Data;2021 IEEE International Conference on Big Data (Big Data);2021-12-15

3. Towards a Learned Index Structure for Approximate Nearest Neighbor Search Query Processing;Similarity Search and Applications;2021

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