research-article

Sparse hashing for fast multimedia search

Authors:

Heng Tao ShenAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 31, Issue 2

Article No.: 9, Pages 1 - 24

https://doi.org/10.1145/2457465.2457469

Published: 17 May 2013 Publication History

Abstract

Hash-based methods achieve fast similarity search by representing high-dimensional data with compact binary codes. However, both generating binary codes and encoding unseen data effectively and efficiently remain very challenging tasks. In this article, we focus on these tasks to implement approximate similarity search by proposing a novel hash based method named sparse hashing (SH for short). To generate interpretable (or semantically meaningful) binary codes, the proposed SH first converts original data into low-dimensional data through a novel nonnegative sparse coding method. SH then converts the low-dimensional data into Hamming space (i.e., binary encoding low-dimensional data) by a new binarization rule. After this, training data are represented by generated binary codes. To efficiently and effectively encode unseen data, SH learns hash functions by taking a-priori knowledge into account, such as implicit group effect of the features in training data, and the correlations between original space and the learned Hamming space. SH is able to perform fast approximate similarity search by efficient bit XOR operations in the memory of a modern PC with short binary code representations. Experimental results show that the proposed SH significantly outperforms state-of-the-art techniques.

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Index Terms

Sparse hashing for fast multimedia search
1. Information systems
  1. Information retrieval
    1. Document representation

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 31, Issue 2

May 2013

180 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2457465

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Copyright © 2013 ACM.

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Association for Computing Machinery

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Publication History

Published: 17 May 2013

Accepted: 01 February 2013

Revised: 01 August 2012

Received: 01 March 2012

Published in TOIS Volume 31, Issue 2

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Cited By

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https://dl.acm.org/doi/10.1609/aaai.v38i11.29120
Piao MSheng YYan JJin C(2024)Image Hash Layer Triggered CNN Framework for Wafer Map Failure Pattern Retrieval and ClassificationACM Transactions on Knowledge Discovery from Data10.1145/363805318:4(1-26)Online publication date: 13-Feb-2024
https://dl.acm.org/doi/10.1145/3638053
Ridani MAmnai M(2024)Query Optimization Using Indexation Techniques in Datawarehouse: Survey and Use CasesArtificial Intelligence, Data Science and Applications10.1007/978-3-031-48465-0_53(406-412)Online publication date: 5-Mar-2024
https://doi.org/10.1007/978-3-031-48465-0_53
Nie XShi YMeng ZHuang JGuan WYin Y(2023)Complex Scenario Image Retrieval via Deep Similarity-aware HashingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362401620:4(1-24)Online publication date: 13-Sep-2023
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Ridani MAmnai MChoukri AFakhri YGherabi N(2023)Indexation techniques in decision support systems: Study and Evaluation2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM)10.1109/WINCOM59760.2023.10322993(1-7)Online publication date: 26-Oct-2023
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Li DDai DChen JXia SWang G(2023)Ensemble learning framework for image retrieval via deep hash rankingKnowledge-Based Systems10.1016/j.knosys.2022.110128260:COnline publication date: 25-Jan-2023
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Khaire UDhanalakshmi R(2022)Stability of feature selection algorithm: A reviewJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2019.06.01234:4(1060-1073)Online publication date: Apr-2022
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