research-article

Stepwise Refinement Short Hashing for Image Retrieval

Authors:

Zhenwen RenAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 6501 - 6509

https://doi.org/10.1145/3581783.3611864

Published: 27 October 2023 Publication History

Abstract

Due to significant advantages in terms of storage cost and query speed, hashing learning has attracted much attention for image retrieval. Existing hashing methods often acquiescently use long hash codes to guarantee performance, which greatly limits flexibility and scalability. Nevertheless, short hash codes are more suitable for devices with limited computing resources. When these methods use extremely short hash codes, it is difficult to meet the actual performance demand due to the information loss caused by the avalanche of dimension truncation. To address this issue, we propose a novel stepwise refinement short hashing (SRSH) for image retrieval that extracts critical features from high-dimensional image data to learn high-quality hash codes. Specifically, we propose a three-step coupled refinement strategy to relax a single hash function into three more flexible mapping matrices, such that the hash function can have more flexible to approximate precise hash codes and alleviate the information loss. Then, we adopt pairwise similarity preserving to promote coarse and fine hash codes to inherit intrinsic semantic structure from original data. Extensive experiments demonstrate the superior performance of SRSH on four image datasets.

Supplemental Material

MP4 File

Stepwise Refinement Short Hashing for Image Retrieval

Download
70.80 MB

References

[1]

Yuan Cao, Sheng Chen, Jie Gui, Heng Qi, Zhiyang Li, and Chao Liu. 2021. Hash learning with variable quantization for large-scale retrieval. IEEE Transactions on Circuits and Systems for Video Technology 32, 5 (2021), 2624--2637.

Digital Library

[2]

Mansheng Chen, Tuo Liu, Chang-Dong Wang, Dong Huang, and Jian-Huang Lai. 2022. Adaptively-weighted Integral Space for Fast Multiview Clustering. In The 30th ACM International Conference on Multimedia. ACM, 3774--3782.

[3]

Mansheng Chen, Chang-Dong Wang, Dong Huang, Jian-Huang Lai, and Philip S. Yu. 2022. Efficient Orthogonal Multi-view Subspace Clustering. In The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 127--135.

[4]

Man-Sheng Chen, Chang-Dong Wang, and Jian-Huang Lai. 2022. Low-rank Tensor Based Proximity Learning for Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering (2022).

[5]

Yong Chen, Zhibao Tian, Hui Zhang, Jun Wang, and Dell Zhang. 2020. Strongly constrained discrete hashing. IEEE Transactions on Image Processing 29 (2020), 3596--3611.

Digital Library

[6]

Hui Cui, Lei Zhu, Jingjing Li, Zhiyong Cheng, and Zheng Zhang. 2021. Twopronged strategy: Lightweight augmented graph network hashing for scalable image retrieval. In Proceedings of the 29th ACM International Conference on Multimedia. 1432--1440.

Digital Library

[7]

Hui Cui, Lei Zhu, Jingjing Li, Yang Yang, and Liqiang Nie. 2019. Scalable deep hashing for large-scale social image retrieval. IEEE Transactions on image processing 29 (2019), 1271--1284.

[8]

Thanh-Toan Do, Khoa Le, Tuan Hoang, Huu Le, Tam V Nguyen, and Ngai-Man Cheung. 2019. Simultaneous feature aggregating and hashing for compact binary code learning. IEEE Transactions on Image Processing 28, 10 (2019), 4954--4969.

[9]

Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, and Tieniu Tan. 2017. Fast supervised discrete hashing. IEEE transactions on pattern analysis and machine intelligence 40, 2 (2017), 490--496.

[10]

Rundong He, Zhongyi Han, Xiankai Lu, and Yilong Yin. 2022. RONF: reliable outlier synthesis under noisy feature space for out-of-distribution detection. In Proceedings of the 30th ACM International Conference on Multimedia. 4242--4251.

Digital Library

[11]

Rundong He, Zhongyi Han, Xiankai Lu, and Yilong Yin. 2022. Safe-student for safe deep semi-supervised learning with unseen-class unlabeled data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14585--14594.

[12]

Rundong He, Zhongyi Han, Yang Yang, and Yilong Yin. 2022. Not all parameters should be treated equally: Deep safe semi-supervised learning under class distribution mismatch. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 6874--6883.

[13]

Shiyuan He, Bokun Wang, Zheng Wang, Yang Yang, Fumin Shen, Zi Huang, and Heng Tao Shen. 2019. Bidirectional discrete matrix factorization hashing for image search. IEEE transactions on cybernetics 50, 9 (2019), 4157--4168.

[14]

Di Hu, Feiping Nie, and Xuelong Li. 2018. Discrete spectral hashing for efficient similarity retrieval. IEEE Transactions on Image Processing 28, 3 (2018), 1080--1091.

[15]

Peng Hu, Xi Peng, Hongyuan Zhu, Jie Lin, Liangli Zhen, and Dezhong Peng. 2021. Joint Versus Independent Multiview Hashing for Cross-View Retrieval. IEEE Transactions on Cybernetics 51, 10 (2021), 4982--4993. https://doi.org/10.1109/ TCYB.2020.3027614

[16]

Peng Hu, XuWang, Liangli Zhen, and Dezhong Peng. 2019. Separated variational hashing networks for cross-modal retrieval. In Proceedings of the 27th ACM International Conference on Multimedia. 1721--1729.

Digital Library

[17]

Peng Hu, Hongyuan Zhu, Jie Lin, Dezhong Peng, Yin-Ping Zhao, and Xi Peng. 2023. Unsupervised Contrastive Cross-Modal Hashing. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 3 (2023), 3877--3889. https://doi. org/10.1109/TPAMI.2022.3177356

[18]

Huaxiong Li, Chao Zhang, Xiuyi Jia, Yang Gao, and Chunlin Chen. 2023. Adaptive Label Correlation Based Asymmetric Discrete Hashing for Cross-Modal Retrieval. IEEE Transactions on Knowledge and Data Engineering 35, 2 (2023), 1185--1199. https://doi.org/10.1109/TKDE.2021.3102119

[19]

Mingbao Lin, Rongrong Ji, Hong Liu, and Yongjian Wu. 2018. Supervised online hashing via hadamard codebook learning. In Proceedings of the 26th ACM international conference on Multimedia. 1635--1643.

Digital Library

[20]

Hong Liu, Rongrong Ji, Jingdong Wang, and Chunhua Shen. 2018. Ordinal constraint binary coding for approximate nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence 41, 4 (2018), 941--955.

[21]

Xingbo Liu, Xiao Kang, Xiushan Nie, Jie Guo, Shaohua Wang, and Yilong Yin. 2022. Learning Binary Semantic Embedding for Large-Scale Breast Histology Image Analysis. IEEE Journal of Biomedical and Health Informatics 26, 7 (2022), 3240--3250. https://doi.org/10.1109/JBHI.2022.3161341

[22]

Xingbo Liu, Xiushan Nie, Qi Dai, Yupan Huang, Li Lian, and Yilong Yin. 2020. Reinforced short-length hashing. IEEE Transactions on Circuits and Systems for Video Technology 31, 9 (2020), 3655--3668.

[23]

Xingbo Liu, Xiushan Nie, Quan Zhou, Xiaoming Xi, Lei Zhu, and Yilong Yin. 2019. Supervised Short-Length Hashing. In IJCAI. 3031--3037.

[24]

Kaiyi Luo, Chao Zhang, Huaxiong Li, Xiuyi Jia, and Chunlin Chen. 2023. Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval. IEEE Transactions on Multimedia (2023), 1--14. https://doi.org/10.1109/TMM.2023.3245400

Digital Library

[25]

Xin Luo, Liqiang Nie, Xiangnan He, Ye Wu, Zhen-Duo Chen, and Xin-Shun Xu. 2018. Fast scalable supervised hashing. In The 41st international ACM SIGIR conference on research & development in information retrieval. 735--744.

Digital Library

[26]

Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, and Xian-Sheng Hua. 2023. A survey on deep hashing methods. ACM Transactions on Knowledge Discovery from Data 17, 1 (2023), 1--50.

[27]

Xin Luo, Ye Wu, and Xin-Shun Xu. 2018. Scalable supervised discrete hashing for large-scale search. In Proceedings of the 2018 World Wide Web Conference. 1603--1612.

Digital Library

[28]

Xin Luo, Peng-Fei Zhang, Zi Huang, Liqiang Nie, and Xin-Shun Xu. 2019. Discrete hashing with multiple supervision. IEEE Transactions on Image Processing 28, 6 (2019), 2962--2975.

[29]

Yadan Luo, Zi Huang, Yang Li, Fumin Shen, Yang Yang, and Peng Cui. 2020. Collaborative learning for extremely low bit asymmetric hashing. IEEE Transactions on Knowledge and Data Engineering 33, 12 (2020), 3675--3685.

[30]

Xiushan Nie, Xingbo Liu, Jie Guo, LetianWang, and Yilong Yin. 2022. Supervised Discrete Multiple-Length Hashing for Image Retrieval. IEEE Transactions on Big Data 01 (2022), 1--1.

[31]

Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015. Supervised discrete hashing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 7. IEEE, 37--45.

[32]

Fumin Shen, Yan Xu, Li Liu, Yang Yang, Zi Huang, and Heng Tao Shen. 2018. Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE transactions on pattern analysis and machine intelligence 40, 12 (2018), 3034--3044.

Digital Library

[33]

Dan Shi, Lei Zhu, Jingjing Li, Zheng Zhang, and Xiaojun Chang. 2023. Unsupervised Adaptive Feature Selection With Binary Hashing. IEEE Transactions on Image Processing 32 (2023), 838--853. https://doi.org/10.1109/TIP.2023.3234497

[34]

Yang Shi, Xiushan Nie, Xingbo Liu, Li Zou, and Yilong Yin. 2022. Supervised Adaptive Similarity Matrix Hashing. IEEE Transactions on Image Processing 31 (2022), 2755--2766.

[35]

Yuan Sun, Dezhong Peng, Haixiao Huang, and Zhenwen Ren. 2022. Feature and Semantic Views Consensus Hashing for Image Set Classification. In Proceedings of the 30th ACM International Conference on Multimedia. 2097--2105.

Digital Library

[36]

Yuan Sun, Xu Wang, Dezhong Peng, Zhenwen Ren, and Xiaobo Shen. 2023. Hierarchical Hashing Learning for Image Set Classification. IEEE Transactions on Image Processing 32 (2023), 1732--1744.

Digital Library

[37]

Xing Tian, Wing W. Y. Ng, and Huihui Xu. 2022. Deep Incremental Hashing for Semantic Image Retrieval with Concept Drift. IEEE Transactions on Big Data (2022), 1--13. https://doi.org/10.1109/TBDATA.2022.3233457

[38]

Jingdong Wang, Ting Zhang, Nicu Sebe, Heng Tao Shen, et al. 2017. A survey on learning to hash. IEEE transactions on pattern analysis and machine intelligence 40, 4 (2017), 769--790.

[39]

Xiangxi Xu, Zhi-Hui Lai, and Yu dong Chen. 2020. Relaxed Locality Preserving Supervised Discrete Hashing. IEEE Transactions on Big Data 01 (2020), 1--1.

[40]

Xihong Yang, Xiaochang Hu, Sihang Zhou, Xinwang Liu, and En Zhu. 2022. Interpolation-based contrastive learning for few-label semi-supervised learning. IEEE Transactions on Neural Networks and Learning Systems (2022).

[41]

Xihong Yang, Yue Liu, Sihang Zhou, Siwei Wang, Wenxuan Tu, Qun Zheng, Xinwang Liu, Liming Fang, and En Zhu. 2023. Cluster-guided Contrastive Graph Clustering Network. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 10834--10842.

Digital Library

[42]

Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, and Jiashi Feng. 2020. Central similarity quantization for efficient image and video retrieval. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 3083--3092.

[43]

Chao Zhang, Huaxiong Li, Yang Gao, and Chunlin Chen. 2023.Weakly-Supervised Enhanced Semantic-Aware Hashing for Cross-Modal Retrieval. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2023), 6475--6488. https://doi.org/10. 1109/TKDE.2022.3172216

Digital Library

[44]

Zheng Zhang, Zhihui Lai, Zi Huang, Wai Keung Wong, Guo-Sen Xie, Li Liu, and Ling Shao. 2019. Scalable supervised asymmetric hashing with semantic and latent factor embedding. IEEE Transactions on Image Processing 28, 10 (2019), 4803--4818.

[45]

Zheng Zhang and Chi-Man Pun. 2022. Learning ordinal constraint binary codes for fast similarity search. Information Processing & Management 59, 3 (2022), 102919.

Digital Library

[46]

Zheng Zhang, Xiaofeng Zhu, Guangming Lu, and Yudong Zhang. 2021. Probability ordinal-preserving semantic hashing for large-scale image retrieval. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 3 (2021), 1--22.

[47]

Chaoqun Zheng, Lei Zhu, Zhiyong Cheng, Jingjing Li, and An-An Liu. 2020. Adaptive partial multi-view hashing for efficient social image retrieval. IEEE Transactions on Multimedia 23 (2020), 4079--4092.

[48]

Lei Zhu, Zi Huang, Zhihui Li, Liang Xie, and Heng Tao Shen. 2018. Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval. IEEE transactions on neural networks and learning systems 29, 11 (2018), 5264--5276.

Cited By

Zhang CXu DJia XChen CLi HLarson K(2024)Continual multi-view clustering with consistent anchor guidanceProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/601(5434-5442)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/601
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
Lei FZhang CLi HGao YChen C(2024)Label Distribution Guided Hashing for Cross-Modal RetrievalACM Transactions on Knowledge Discovery from Data10.1145/3697353Online publication date: 26-Sep-2024
https://dl.acm.org/doi/10.1145/3697353
Show More Cited By

Index Terms

Stepwise Refinement Short Hashing for Image Retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Recommendations

Manhattan hashing for large-scale image retrieval
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Hashing is used to learn binary-code representation for data with expectation of preserving the neighborhood structure in the original feature space. Due to its fast query speed and reduced storage cost, hashing has been widely used for efficient ...
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Similarity search is a key challenge for multimedia retrieval applications where data are usually represented in high-dimensional space. Among various algorithms proposed for similarity search in high-dimensional space, Locality-Sensitive Hashing (LSH) ...
Order preserving hashing for approximate nearest neighbor search
MM '13: Proceedings of the 21st ACM international conference on Multimedia

In this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 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 the author(s) 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

the Open Research Fund of Anhui Province Key Laboratory of Machine Vision Inspection
the National Natural Science Foundation of China
the Chengdu Science and Technology Project
the Fundamental Research Funds for the Central Universities
Sichuan Science and Technology Planning Project

Conference

MM '23

Sponsor:

SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
147
Total Downloads

Downloads (Last 12 months)67
Downloads (Last 6 weeks)4

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang CXu DJia XChen CLi HLarson K(2024)Continual multi-view clustering with consistent anchor guidanceProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/601(5434-5442)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/601
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
Lei FZhang CLi HGao YChen C(2024)Label Distribution Guided Hashing for Cross-Modal RetrievalACM Transactions on Knowledge Discovery from Data10.1145/3697353Online publication date: 26-Sep-2024
https://dl.acm.org/doi/10.1145/3697353
Sun YQin YPeng DRen ZYang CHu P(2024)Dual Self-Paced Hashing for Image RetrievalIEEE Transactions on Multimedia10.1109/TMM.2024.339596926(9619-9629)Online publication date: 2-May-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3395969
Huo YQin QZhang WHuang LNie J(2024)Deep Hierarchy-Aware Proxy Hashing With Self-Paced Learning for Cross-Modal RetrievalIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.340105036:11(5926-5939)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3401050

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten