research-article

Distributed KNN-graph approximation via hashing

Authors:

Mohamed Riadh Trad,

Nozha BoujemaaAuthors Info & Claims

ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

Article No.: 43, Pages 1 - 8

https://doi.org/10.1145/2324796.2324847

Published: 05 June 2012 Publication History

Abstract

Efficiently constructing the K-Nearest Neighbor Graph (K-NNG) of large and high dimensional datasets is crucial for many applications with feature-rich objects, such as images or other multimedia content. In this paper we investigate the use of high dimensional hashing methods for efficiently approximating the K-NNG, notably in distributed environments. We first discuss the importance of balancing issues on the performance of such approaches and show why the baseline approach using Locality Sensitive Hashing does not perform well. Our new KNN-join method is based on RMMH, a recently introduced hash function family based on randomly trained classifiers. We show that the resulting hash tables are much more balanced and that the number of resulting collisions can be greatly reduced without degrading quality. We further improve the load balancing of our distributed approach by designing a parallelized local join algorithm, implemented within the MapReduce framework.

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Cited By

Adhikary SBanerjee S(2023)Introduction to Distributed Nearest HashProcedia Computer Science10.1016/j.procs.2023.01.135218:C(1571-1580)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.01.135
Zhang TGao YZheng BChen LWen SGuo W(2020)Towards distributed node similarity search on graphsWorld Wide Web10.1007/s11280-020-00819-623:6(3025-3053)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1007/s11280-020-00819-6
Ma HLi J(2019)A True $$O(n\log {n})$$ Algorithm for the All-k-Nearest-Neighbors ProblemCombinatorial Optimization and Applications10.1007/978-3-030-36412-0_29(362-374)Online publication date: 23-Nov-2019
https://doi.org/10.1007/978-3-030-36412-0_29
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Index Terms

Distributed KNN-graph approximation via hashing
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

June 2012

489 pages

ISBN:9781450313292

DOI:10.1145/2324796

Conference Chairs:
Horace H. S. Ip
City University of Hong Kong
,
Yong Rui
Microsoft, China

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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New York, NY, United States

Publication History

Published: 05 June 2012

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Seventh Framework Programme

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ICMR '12

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SIGMM

ICMR '12: International Conference on Multimedia Retrieval

June 5 - 8, 2012

Hong Kong, China

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ICMR '12 Paper Acceptance Rate 50 of 145 submissions, 34%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Adhikary SBanerjee S(2023)Introduction to Distributed Nearest HashProcedia Computer Science10.1016/j.procs.2023.01.135218:C(1571-1580)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.procs.2023.01.135
Zhang TGao YZheng BChen LWen SGuo W(2020)Towards distributed node similarity search on graphsWorld Wide Web10.1007/s11280-020-00819-623:6(3025-3053)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1007/s11280-020-00819-6
Ma HLi J(2019)A True $$O(n\log {n})$$ Algorithm for the All-k-Nearest-Neighbors ProblemCombinatorial Optimization and Applications10.1007/978-3-030-36412-0_29(362-374)Online publication date: 23-Nov-2019
https://doi.org/10.1007/978-3-030-36412-0_29
Zhang LWang ZMei TFeng D(2016)A Scalable Approach for Content-Based Image Retrieval in Peer-to-Peer NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.250528428:4(858-872)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2505284
Zhang HSun ZLiu ZXu CWang L(2015)DartProceedings of the 2015 IEEE 8th International Conference on Cloud Computing10.1109/CLOUD.2015.22(90-97)Online publication date: 27-Jun-2015
https://dl.acm.org/doi/10.1109/CLOUD.2015.22

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