research-article

Learning Hash Functions Using Sparse Reconstruction

Authors:

Yong Yuan,

Xiaoqiang Lu,

Xuelong LiAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 14 - 18

https://doi.org/10.1145/2632856.2632883

Published: 10 July 2014 Publication History

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Abstract

Approximate nearest neighbor (ANN) search is becoming an increasingly important technique in large-scale problems. Recently many approaches have been developed due to fast query and low storage cost. Although most of them have realized the importance of the data structure, they neglected the sparse relationship of the data. To build a balance between the adjusted covariance matrix and the minimum reconstruction error of data points, this paper proposes a novel method based on sparse reconstruction to learn more compact binary codes under l2,1-norm constraint. Experiments demonstrate that the proposed method, named as sparse reconstruction hashing, outperforms several other state-of-the-art methods when tested on a few benchmark datasets.

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Cited By

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Cui CHuo HFang T(2023)Deep Hashing With Multi-Central Ranking Loss for Multi-Label Image RetrievalIEEE Signal Processing Letters10.1109/LSP.2023.324451630(135-139)Online publication date: 2023
https://doi.org/10.1109/LSP.2023.3244516
Omara AFarouk HMohamed S(2022)nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded ImagesIEEE Access10.1109/ACCESS.2022.317045310(46067-46081)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3170453
Zhang HGu YYao YZhang ZLiu LZhang JShao L(2021)Deep Unsupervised Self-Evolutionary Hashing for Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2020.302500023(3400-3413)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.3025000
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Index Terms

Learning Hash Functions Using Sparse Reconstruction
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

Information

Published In

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

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]

In-Cooperation

NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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Conference

ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

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Cui CHuo HFang T(2023)Deep Hashing With Multi-Central Ranking Loss for Multi-Label Image RetrievalIEEE Signal Processing Letters10.1109/LSP.2023.324451630(135-139)Online publication date: 2023
https://doi.org/10.1109/LSP.2023.3244516
Omara AFarouk HMohamed S(2022)nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded ImagesIEEE Access10.1109/ACCESS.2022.317045310(46067-46081)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3170453
Zhang HGu YYao YZhang ZLiu LZhang JShao L(2021)Deep Unsupervised Self-Evolutionary Hashing for Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2020.302500023(3400-3413)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.3025000
Thapa MMulani SWalters R(2019) Efficient Adaptive Sparse Polynomial Chaos Expansion with l 1 -minimization and Sequential Sampling AIAA Scitech 2019 Forum10.2514/6.2019-1226Online publication date: 6-Jan-2019
https://doi.org/10.2514/6.2019-1226
Zhang HLiu LLong YShao L(2018)Unsupervised Deep Hashing With Pseudo Labels for Scalable Image RetrievalIEEE Transactions on Image Processing10.1109/TIP.2017.278142227:4(1626-1638)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1109/TIP.2017.2781422
Mou HBarbu A(2018)Accurate Dictionary Learning with Direct Sparsity Control2018 25th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2018.8451562(1842-1846)Online publication date: Oct-2018
https://doi.org/10.1109/ICIP.2018.8451562
Yang ZYue JLi ZZhu L(2018)Vegetable Image Retrieval with Fine-tuning VGG Model and Image HashIFAC-PapersOnLine10.1016/j.ifacol.2018.08.17551:17(280-285)Online publication date: 2018
https://doi.org/10.1016/j.ifacol.2018.08.175
Ye RLi X(2016)Compact Structure Hashing via Sparse and Similarity Preserving EmbeddingIEEE Transactions on Cybernetics10.1109/TCYB.2015.241429946:3(718-729)Online publication date: Mar-2016
https://doi.org/10.1109/TCYB.2015.2414299
Zhang ZXu YYang JLi XZhang D(2015)A Survey of Sparse Representation: Algorithms and ApplicationsIEEE Access10.1109/ACCESS.2015.24303593(490-530)Online publication date: 2015
https://doi.org/10.1109/ACCESS.2015.2430359

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