research-article

Rank-embedded Hashing for Large-scale Image Retrieval

Authors:

Hengheng Zhang,

Tao YaoAuthors Info & Claims

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

Pages 563 - 570

https://doi.org/10.1145/3372278.3390716

Published: 08 June 2020 Publication History

Abstract

With the growth of images on the Internet, plenty of hashing methods are developed to handle the large-scale image retrieval task. Hashing methods map data from high dimension to compact codes, so that they can effectively cope with complicated image features. However, the quantization process of hashing results in unescapable information loss. As a consequence, it is a challenge to measure the similarity between images with generated binary codes. The latest works usually focus on learning deep features and hashing functions simultaneously to preserve the similarity between images, while the similarity metric is fixed. In this paper, we propose a Rank-embedded Hashing (ReHash) algorithm where the ranking list is automatically optimized together with the feedback of the supervised hashing. Specifically, ReHash jointly trains the metric learning and the hashing codes in an end-to-end model. In this way, the similarity between images are enhanced by the ranking process. Meanwhile, the ranking results are an additional supervision for the hashing function learning as well. Extensive experiments show that our ReHash outperforms the state-of-the-art hashing methods for large-scale image retrieval.

References

[1]

Song Bai, Peng Tang, Philip HS Torr, and Longin Jan Latecki. 2019. Re-ranking via metric fusion for object retrieval and person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 740--749.

[2]

Bing-Kun Bao, Changsheng Xu, Weiqing Min, and Mohammod Shamim Hossain. 2015. Cross-platform emerging topic detection and elaboration from multimedia streams. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 11, 4 (2015), 54.

[3]

Yue Cao, Mingsheng Long, Liu Bin, and Jianmin Wang. 2018. Deep Cauchy Hashing for Hamming Space Retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[4]

Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, and Qingfu Wen. 2016. Deep Quantization Network for Efficient Image Retrieval. In Proceedings of the Association for the Advance of Artificial Intelligence.

[5]

Zhangjie Cao, Mingsheng Long, Jianmin Wang, and S. Philip Yu. 2017. HashNet: Deep Learning to Hash by Continuation. In Proceedings of the IEEE International Conference on Computer Vision.

[6]

Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the devil in the details: Delving deep into convolutional nets. In British Machine Vision Conference.

[7]

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. 48:1--48:9.

[8]

Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, and Jie Zhou. 2015. Deep hashing for compact binary codes learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2475--2483.

[9]

Haiyan Fu, Xiangwei Kong, and Zhenfan Wang. 2016a. Binary code reranking method with weighted hamming distance. Multimedia Tools and Applications, Vol. 75, 3 (2016), 1391--1408.

Digital Library

[10]

Haiyan Fu, Hanguang Zhao, Xiangwei Kong, and Xianbo Zhang. 2016b. BHoG: binary descriptor for sketch-based image retrieval. Multimedia Systems, Vol. 22, 1 (2016), 127--136.

Digital Library

[11]

Lianli Gao, Jingkuan Song, Fuhao Zou, Dongxiang Zhang, and Jie Shao. 2015. Scalable multimedia retrieval by deep learning hashing with relative similarity learning. In Proceedings of the 23rd ACM international conference on Multimedia. ACM, 903--906.

Digital Library

[12]

Weifeng Ge. 2018. Deep metric learning with hierarchical triplet loss. In Proceedings of the European Conference on Computer Vision (ECCV). 269--285.

Digital Library

[13]

Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2012. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 12 (2012), 2916--2929.

Digital Library

[14]

Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2. IEEE, 1735--1742.

Digital Library

[15]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.

[16]

Qing-Yuan Jiang and Wu-Jun Li. 2015. Scalable graph hashing with feature transformation. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

[17]

Qing-Yuan Jiang and Wu-Jun Li. 2018. Asymmetric deep supervised hashing. In Thirty-Second AAAI Conference on Artificial Intelligence.

[18]

Yu-Gang Jiang, Jun Wang, and Shih-Fu Chang. 2011. Lost in binarization: query-adaptive ranking for similar image search with compact codes. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval. ACM, 16.

Digital Library

[19]

Wang-Cheng Kang, Wu-Jun Li, and Zhi-Hua Zhou. 2016. Column sampling based discrete supervised hashing. In Thirtieth AAAI conference on artificial intelligence.

Digital Library

[20]

Weihao Kong and Wu-Jun Li. 2012. Isotropic hashing. In Advances in neural information processing systems. 1646--1654.

[21]

Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. Technical Report. University of Toronto.

[22]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.

[23]

Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. 2015. Simultaneous feature learning and hash coding with deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3270--3278.

[24]

Ning Li, Chao Li, Cheng Deng, Xianglong Liu, and Xinbo Gao. 2018b. Deep joint semantic-embedding hashing. In Proceedings of theTwenty-Seventh International Joint Conference on Artifiial Intelligence.

[25]

Peng Li, Meng Wang, Jian Cheng, Changsheng Xu, and Hanqing Lu. 2012. Spectral hashing with semantically consistent graph for image indexing. IEEE Transactions on Multimedia, Vol. 15, 1 (2012), 141--152.

Digital Library

[26]

Qiang Li, Haiyan Fu, Xiangwei Kong, and Qi Tian. 2018a. Deep hashing with top similarity preserving for image retrieval. Multimedia Tools and Applications, Vol. 77, 18 (2018), 24121--24141.

Digital Library

[27]

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.

Digital Library

[28]

Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton Van den Hengel, and David Suter. 2014b. Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1963--1970.

Digital Library

[29]

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014a. Microsoft coco: Common objects in context. In European conference on computer vision. Springer, 740--755.

[30]

Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, and Jingdong Wang. 2018. Deep Triplet Quantization. In Proceedings of the ACM international conference on Multimedia.

Digital Library

[31]

Wei Liu, Cun Mu, Sanjiv Kumar, and Shih-Fu Chang. 2014b. Discrete graph hashing. In Advances in neural information processing systems. 3419--3427.

[32]

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. IEEE, 2074--2081.

Digital Library

[33]

Zhen Liu, Houqiang Li, Wengang Zhou, Ruizhen Zhao, and Qi Tian. 2014a. Contextual hashing for large-scale image search. IEEE Transactions on Image Processing, Vol. 23, 4 (2014), 1606--1614.

Digital Library

[34]

Xuan Ma, Bing-Kun Bao, Lingling Yao, and Changsheng Xu. 2019. Multimodal Latent Factor Model with Language Constraint for Predicate Detection. In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 4454--4458.

[35]

Behnam Neyshabur, Nati Srebro, Ruslan R Salakhutdinov, Yury Makarychev, and Payman Yadollahpour. 2013. The power of asymmetry in binary hashing. In Advances in Neural Information Processing Systems. 2823--2831.

[36]

Fudong Nian, Bing-Kun Bao, Teng Li, and Changsheng Xu. 2017. Multi-modal knowledge representation learning via webly-supervised relationships mining. In Proceedings of the 25th ACM international conference on Multimedia. ACM, 411--419.

Digital Library

[37]

Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. 2016. Deep metric learning via lifted structured feature embedding. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4004--4012.

[38]

Fumin Shen, Xin Gao, Li Liu, Yang Yang, and Heng Tao Shen. 2017. Deep asymmetric pairwise hashing. In Proceedings of the 25th ACM international conference on Multimedia. 1522--1530.

Digital Library

[39]

Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015. Supervised discrete hashing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 37--45.

[40]

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, Vol. 40, 12 (2018), 3034--3044.

Digital Library

[41]

Xiaoshuang Shi, Fuyong Xing, Kaidi Xu, Manish Sapkota, and Lin Yang. 2017. Asymmetric discrete graph hashing. In Thirty-First AAAI Conference on Artificial Intelligence.

Digital Library

[42]

Kihyuk Sohn. 2016. Improved deep metric learning with multi-class n-pair loss objective. In Advances in neural information processing systems. 1857--1865.

[43]

Junyi Wang, Bing-Kun Bao, and Changsheng Xu. 2019 a. Sentiment-Aware Multi-modal Recommendation on Tourist Attractions. In International Conference on Multimedia Modeling. Springer, 3--16.

[44]

Jianfeng Wang, Jingdong Wang, Nenghai Yu, and Shipeng Li. 2013. Order preserving hashing for approximate nearest neighbor search. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 133--142.

Digital Library

[45]

Xinshao Wang, Yang Hua, Elyor Kodirov, Guosheng Hu, Romain Garnier, and Neil M Robertson. 2019 c. Ranked list loss for deep metric learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5207--5216.

[46]

Xiaofang Wang, Yi Shi, and Kris M Kitani. 2016. Deep supervised hashing with triplet labels. In Asian conference on computer vision. Springer, 70--84.

[47]

Yinghao Wang, Chen Chen, Jiong Wang, and Yingying Zhu. 2019 b. Learning Discriminative Features for Image Retrieval. In Proceedings of the 2019 on International Conference on Multimedia Retrieval. ACM, 96--104.

Digital Library

[48]

Yair Weiss, Antonio Torralba, and Rob Fergus. 2009. Spectral hashing. In Advances in neural information processing systems. 1753--1760.

[49]

Xinyi Xu, Yanhua Yang, Cheng Deng, and Feng Zheng. 2019. Deep asymmetric metric learning via rich relationship mining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4076--4085.

[50]

Lei Zhang, Yongdong Zhang, Jinhui Tang, Xiaoguang Gu, Jintao Li, and Qi Tian. 2013. Topology preserving hashing for similarity search. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 123--132.

Digital Library

[51]

Peichao Zhang, Wei Zhang, Wu-Jun Li, and Minyi Guo. 2014. Supervised hashing with latent factor models. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 173--182.

Digital Library

[52]

Fang Zhao, Yongzhen Huang, Liang Wang, and Tieniu Tan. 2015. Deep semantic ranking based hashing for multi-label image retrieval. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1556--1564.

[53]

Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. 2016. Deep Hashing Network for Efficient Similarity Retrieval. In Proceedings of the Association for the Advance of Artificial Intelligence.

[54]

Yueting Zhuang, Yang Liu, Fei Wu, Yin Zhang, and Jian Shao. 2011. Hypergraph spectral hashing for similarity search of social image. In Proceedings of the 19th ACM international conference on Multimedia. ACM, 1457--1460.

Digital Library

Cited By

Li YGuan CGao J(2023)TsP-Tran: Two-Stage Pure Transformer for Multi-Label Image RetrievalProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592269(425-433)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592269
Pegia MMoumtzidou AGialampoukidis IJonsson BVrochidis SKompatsiaris I(2022)BiasHash: A Bayesian Hashing Framework for Image Retrieval2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)10.1109/IVMSP54334.2022.9816233(1-5)Online publication date: 26-Jun-2022
https://doi.org/10.1109/IVMSP54334.2022.9816233
Wu LChen YHu WShi G(2021)Exponential Hashing with Different Penalty for Hamming Space RetrievalImage and Graphics10.1007/978-3-030-87355-4_64(772-784)Online publication date: 6-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-87355-4_64

Index Terms

Rank-embedded Hashing for Large-scale Image Retrieval
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Image search

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 ...
Large-scale image retrieval based on boosting iterative quantization hashing with query-adaptive reranking

Image hashing based Approximate Nearest Neighbor (ANN) searching has drawn more and more attention in large-scale image dataset applications. It is still challenging to learn hashing codes to achieve good search performance. In this paper, we propose an ...
Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval
ICMR '19: Proceedings of the 2019 on International Conference on Multimedia Retrieval

We propose an unsupervised hashing method, exploiting a shallow neural network, that aims to produce binary codes that preserve the ranking induced by an original real-valued representation. This is motivated by the emergence of small-world graph-based ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

June 2020

605 pages

ISBN:9781450370875

DOI:10.1145/3372278

General Chairs:
Cathal Gurrin
Dublin City University, Ireland
,
Björn Þór Jónsson
IT University of Copenhagen, Denmark
,
Noriko Kando
National Institute of Informatics, Tokyo
,
Program Chairs:
Klaus Schoeffmann
Klagenfurt University, Austria
,
Phoebe Chen
La Trobe University, Australia
,
Noel E. O'Connor
Dublin City University, Ireland

Copyright © 2020 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: 08 June 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China

Conference

ICMR '20

Sponsor:

SIGMM

ICMR '20: International Conference on Multimedia Retrieval

June 8 - 11, 2020

Dublin, Ireland

Acceptance Rates

Overall Acceptance Rate 254 of 830 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
221
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li YGuan CGao J(2023)TsP-Tran: Two-Stage Pure Transformer for Multi-Label Image RetrievalProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592269(425-433)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592269
Pegia MMoumtzidou AGialampoukidis IJonsson BVrochidis SKompatsiaris I(2022)BiasHash: A Bayesian Hashing Framework for Image Retrieval2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)10.1109/IVMSP54334.2022.9816233(1-5)Online publication date: 26-Jun-2022
https://doi.org/10.1109/IVMSP54334.2022.9816233
Wu LChen YHu WShi G(2021)Exponential Hashing with Different Penalty for Hamming Space RetrievalImage and Graphics10.1007/978-3-030-87355-4_64(772-784)Online publication date: 6-Aug-2021
https://dl.acm.org/doi/10.1007/978-3-030-87355-4_64

View Options

Get Access

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.

Media

Figures

Other

Tables

View Table of Contents