short-paper

Deep Multiple Length Hashing via Multi-task Learning

Authors:

Xingbo LiuAuthors Info & Claims

MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

Article No.: 52, Pages 1 - 5

https://doi.org/10.1145/3469877.3493591

Published: 10 January 2022 Publication History

Abstract

Hashing can compress heterogeneous high-dimensional data into compact binary codes. For most existing hash methods, they first predetermine a fixed length for the hash code and then train the model based on this fixed length. However, when the task requirements change, these methods need to retrain the model for a new length of hash codes, which increases time cost. To address this issue, we propose a deep supervised hashing method, called deep multiple length hashing(DMLH), which can learn multiple length hash codes simultaneously based on a multi-task learning network. This proposed DMLH can well utilize the relationships with a hard parameter sharing-based multi-task network. Specifically, in DMLH, the multiple hash codes with different lengths are regarded as different views of the same sample. Furthermore, we introduce a type of mutual information loss to mine the association among hash codes of different lengths. Extensive experiments have indicated that DMLH outperforms most existing models, verifying its effectiveness.

References

[1]

Alexandr Andoni and Piotr Indyk. 2006. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions. In 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2006), 21-24 October 2006, Berkeley, California, USA, Proceedings. IEEE Computer Society, 459–468. https://doi.org/10.1109/FOCS.2006.49

Digital Library

[2]

Yue Cao, Mingsheng Long, Bin Liu, and Jianmin Wang. 2018. Deep Cauchy Hashing for Hamming Space Retrieval. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018. IEEE Computer Society, 1229–1237. https://doi.org/10.1109/CVPR.2018.00134

[3]

Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Philip S. Yu. 2017. HashNet: Deep Learning to Hash by Continuation. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. IEEE Computer Society, 5609–5618. https://doi.org/10.1109/ICCV.2017.598

[4]

Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the Devil in the Details: Delving Deep into Convolutional Nets. CoRR abs/1405.3531(2014). arxiv:1405.3531http://arxiv.org/abs/1405.3531

[5]

Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. 2009. NUS-WIDE: a real-world web image database from National University of Singapore. In Proceedings of the 8th ACM International Conference on Image and Video Retrieval, CIVR 2009, Santorini Island, Greece, July 8-10, 2009, Stéphane Marchand-Maillet and Yiannis Kompatsiaris (Eds.). ACM. https://doi.org/10.1145/1646396.1646452

Digital Library

[6]

Yunchao Gong and Svetlana Lazebnik. 2011. Iterative quantization: A procrustean approach to learning binary codes. In The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, USA, 20-25 June 2011. IEEE Computer Society, 817–824. https://doi.org/10.1109/CVPR.2011.5995432

Digital Library

[7]

Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2013. Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35, 12 (2013), 2916–2929. https://doi.org/10.1109/TPAMI.2012.193

Digital Library

[8]

Rong Kang, Yue Cao, Mingsheng Long, Jianmin Wang, and Philip S. Yu. 2019. Maximum-Margin Hamming Hashing. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. IEEE, 8251–8260. https://doi.org/10.1109/ICCV.2019.00834

[9]

Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. 2015. Simultaneous feature learning and hash coding with deep neural networks. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. IEEE Computer Society, 3270–3278. https://doi.org/10.1109/CVPR.2015.7298947

[10]

Wu-Jun Li, Sheng Wang, and Wang-Cheng Kang. 2016. Feature Learning Based Deep Supervised Hashing with Pairwise Labels. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, Subbarao Kambhampati (Ed.). IJCAI/AAAI Press, 1711–1717. http://www.ijcai.org/Abstract/16/245

[11]

Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V(Lecture Notes in Computer Science, Vol. 8693), David J. Fleet, Tomás Pajdla, Bernt Schiele, and Tinne Tuytelaars (Eds.). Springer, 740–755. https://doi.org/10.1007/978-3-319-10602-1_48

[12]

Wei Liu, Cun Mu, Sanjiv Kumar, and Shih-Fu Chang. 2014. Discrete Graph Hashing. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 3419–3427. https://proceedings.neurips.cc/paper/2014/hash/f63f65b503e22cb970527f23c9ad7db1-Abstract.html

[13]

Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. 2012. Supervised hashing with kernels. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, June 16-21, 2012. IEEE Computer Society, 2074–2081. https://doi.org/10.1109/CVPR.2012.6247912

[14]

Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. 2011. Hashing with Graphs. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011, Lise Getoor and Tobias Scheffer (Eds.). Omnipress, 1–8. https://icml.cc/2011/papers/6_icmlpaper.pdf

[15]

Xu Lu, Lei Zhu, Zhiyong Cheng, Liqiang Nie, and Huaxiang Zhang. 2019. Online Multi-modal Hashing with Dynamic Query-adaption. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, Benjamin Piwowarski, Max Chevalier, Éric Gaussier, Yoelle Maarek, Jian-Yun Nie, and Falk Scholer (Eds.). ACM, 715–724. https://doi.org/10.1145/3331184.3331217

Digital Library

[16]

Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015. Supervised Discrete Hashing. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. IEEE Computer Society, 37–45. https://doi.org/10.1109/CVPR.2015.7298598

[17]

Jun Wang, Wei Liu, Andy X. Sun, and Yu-Gang Jiang. 2013. Learning Hash Codes with Listwise Supervision. In IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, December 1-8, 2013. IEEE Computer Society, 3032–3039. https://doi.org/10.1109/ICCV.2013.377

Digital Library

[18]

Jianfeng Wang, Jingdong Wang, Nenghai Yu, and Shipeng Li. 2013. Order preserving hashing for approximate nearest neighbor search. In ACM Multimedia Conference, MM ’13, Barcelona, Spain, October 21-25, 2013, Alejandro Jaimes, Nicu Sebe, Nozha Boujemaa, Daniel Gatica-Perez, David A. Shamma, Marcel Worring, and Roger Zimmermann (Eds.). ACM, 133–142. https://doi.org/10.1145/2502081.2502100

Digital Library

[19]

Qifan Wang, Zhiwei Zhang, and Luo Si. 2015. Ranking Preserving Hashing for Fast Similarity Search. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, Qiang Yang and Michael J. Wooldridge (Eds.). AAAI Press, 3911–3917. http://ijcai.org/Abstract/15/549

[20]

Rongkai Xia, Yan Pan, Hanjiang Lai, Cong Liu, and Shuicheng Yan. 2014. Supervised Hashing for Image Retrieval via Image Representation Learning. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada, Carla E. Brodley and Peter Stone (Eds.). AAAI Press, 2156–2162. http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8137

Digital Library

[21]

Chang Xu, Dacheng Tao, and Chao Xu. 2013. A Survey on Multi-view Learning. CoRR abs/1304.5634(2013). arxiv:1304.5634http://arxiv.org/abs/1304.5634

[22]

Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. 2016. Deep Hashing Network for Efficient Similarity Retrieval. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA, Dale Schuurmans and Michael P. Wellman (Eds.). AAAI Press, 2415–2421. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12039

Digital Library

[23]

Lei Zhu, Xu Lu, Zhiyong Cheng, Jingjing Li, and Huaxiang Zhang. 2020. Deep Collaborative Multi-View Hashing for Large-Scale Image Search. IEEE Trans. Image Process. 29 (2020), 4643–4655. https://doi.org/10.1109/TIP.2020.2974065

Digital Library

Cited By

Meng ZWang LDong FNie X(2022)Deep Hashing via Dynamic Similarity Learning for Image Retrieval2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)10.1109/CCIS57298.2022.10016334(239-245)Online publication date: 26-Nov-2022
https://doi.org/10.1109/CCIS57298.2022.10016334

Index Terms

Deep Multiple Length Hashing via Multi-task Learning

Index terms have been assigned to the content through auto-classification.

Recommendations

Deep Adaptive Attention Triple Hashing
MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

Recent studies have verified that learning compact hash codes can facilitate big data retrieval processing. In particular, learning the deep hash function can greatly improve the retrieval performance. However, the existing deep supervised hashing ...
A Survey on Deep Hashing Methods
Nearest neighbor search aims at obtaining the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used ...
Joint learning based deep supervised hashing for large-scale image retrieval
Abstract
Hashing has been widely used for large-scale image retrieval due to its high storage efficiency and fast calculation speed. Recent works have found that deep-supervised hashing methods are superior to non-deep-supervised hashing methods and ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MMAsia '21: Proceedings of the 3rd ACM International Conference on Multimedia in Asia

December 2021

508 pages

ISBN:9781450386074

DOI:10.1145/3469877

Copyright © 2021 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 January 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

Taishan Scholar Project of Shandong Province
The National Natural Science Foundation of China

Conference

MMAsia '21

Sponsor:

SIGMM

MMAsia '21: ACM Multimedia Asia

December 1 - 3, 2021

Gold Coast, Australia

Acceptance Rates

Overall Acceptance Rate 59 of 204 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
58
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 31 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Meng ZWang LDong FNie X(2022)Deep Hashing via Dynamic Similarity Learning for Image Retrieval2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)10.1109/CCIS57298.2022.10016334(239-245)Online publication date: 26-Nov-2022
https://doi.org/10.1109/CCIS57298.2022.10016334

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.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten