research-article

Asymmetric Discrete Cross-Modal Hashing

Authors:

Peng-Fei Zhang,

Hua-Junjie Huang,

Xin-Shun XuAuthors Info & Claims

ICMR '18: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval

Pages 204 - 212

https://doi.org/10.1145/3206025.3206034

Published: 05 June 2018 Publication History

Abstract

Recently, cross-modal hashing (CMH) methods have attracted much attention. Many methods have been explored; however, there are still some issues that need to be further considered. 1) How to efficiently construct the correlations among heterogeneous modalities. 2) How to solve the NP-hard optimization problem and avoid the large quantization errors generated by relaxation. 3) How to handle the complex and difficult problem in most CMH methods that simultaneously learning the hash codes and hash functions. To address these challenges, we present a novel cross-modal hashing algorithm, named Asymmetric Discrete Cross-Modal Hashing (ADCH). Specifically, it leverages the collective matrix factorization technique to learn the common latent representations while preserving not only the cross-correlation from different modalities but also the semantic similarity. Instead of relaxing the binary constraints, it generates the hash codes directly using an iterative optimization algorithm proposed in this work. Based the learnt hash codes, ADCH further learns a series of binary classifiers as hash functions, which is flexible and effective. Extensive experiments are conducted on three real-world datasets. The results demonstrate that ADCH outperforms several state-of-the-art cross-modal hashing baselines.

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Cited By

Liu KGong YCao YRen ZPeng DSun YLarson K(2024)Dual semantic fusion hashing for multi-label cross-modal retrievalProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/505(4569-4577)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/505
Zhu LZheng CGuan WLi JYang YShen H(2024)Multi-Modal Hashing for Efficient Multimedia Retrieval: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328292136:1(239-260)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3282921
Yang ZDeng XGuo LLong J(2024)Asymmetric Supervised Fusion-Oriented Hashing for Cross-Modal RetrievalIEEE Transactions on Cybernetics10.1109/TCYB.2023.324101854:2(851-864)Online publication date: Feb-2024
https://doi.org/10.1109/TCYB.2023.3241018
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Index Terms

Asymmetric Discrete Cross-Modal Hashing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Visual content-based indexing and retrieval
  2. Machine learning
    1. Learning paradigms
2. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval

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Published In

cover image ACM Conferences

ICMR '18: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval

June 2018

550 pages

ISBN:9781450350464

DOI:10.1145/3206025

Conference Chairs:
Kiyoharu Aizawa
The Univ. of Tokyo, Japan
,
Michael Lew
Leiden Univ., Netherlands
,
Shin'ichi Satoh
National Inst. of Informatics, Japan

Copyright © 2018 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2018

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Funding Sources

National Natural Science Foundation of China
Key Research and Development Program of Shandong Province

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ICMR '18

Sponsor:

SIGMM

ICMR '18: International Conference on Multimedia Retrieval

June 11 - 14, 2018

Yokohama, Japan

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ICMR '18 Paper Acceptance Rate 44 of 136 submissions, 32%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Liu KGong YCao YRen ZPeng DSun YLarson K(2024)Dual semantic fusion hashing for multi-label cross-modal retrievalProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/505(4569-4577)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/505
Zhu LZheng CGuan WLi JYang YShen H(2024)Multi-Modal Hashing for Efficient Multimedia Retrieval: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328292136:1(239-260)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3282921
Yang ZDeng XGuo LLong J(2024)Asymmetric Supervised Fusion-Oriented Hashing for Cross-Modal RetrievalIEEE Transactions on Cybernetics10.1109/TCYB.2023.324101854:2(851-864)Online publication date: Feb-2024
https://doi.org/10.1109/TCYB.2023.3241018
Wang SZhao HZhang ZLi K(2024)Individual mapping and asymmetric dual supervision for discrete cross-modal hashingExpert Systems with Applications10.1016/j.eswa.2024.123333247(123333)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.123333
Liu XWang XCheung Y(2022)FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal RetrievalIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.307668433:11(6306-6320)Online publication date: Nov-2022
https://doi.org/10.1109/TNNLS.2021.3076684
Wang LZareapoor MYang JZheng Z(2022)Asymmetric Correlation Quantization Hashing for Cross-Modal RetrievalIEEE Transactions on Multimedia10.1109/TMM.2021.310582424(3665-3678)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3105824
Chen YZhang HTian ZWang JZhang DLi X(2022)Enhanced Discrete Multi-Modal Hashing: More Constraints Yet Less Time to LearnIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299519534:3(1177-1190)Online publication date: 1-Mar-2022
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Qin JFei LZhang ZWen JXu YZhang D(2022)Joint Specifics and Consistency Hash Learning for Large-Scale Cross-Modal RetrievalIEEE Transactions on Image Processing10.1109/TIP.2022.319505931(5343-5358)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3195059
Yang ZDeng XLong J(2022)Fast unsupervised consistent and modality-specific hashing for multimedia retrievalNeural Computing and Applications10.1007/s00521-022-08008-435:8(6207-6223)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-08008-4
Long JSun LHua LYang Z(2021)Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia RetrievalApplied Sciences10.3390/app1118876911:18(8769)Online publication date: 21-Sep-2021
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