Cited By
View all- Yang YZhang JWang QLiu B(2020)Deep High-order Asymmetric Supervised Hashing for Image Retrieval2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207475(1-7)Online publication date: Jul-2020
Deep Policy Hashing Network with Listwise Supervision
Pages 123 - 131
Abstract
Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing. These manners ignore the fact that hashing is a prediction task on the list of binary codes. However, learning deep hashing with listwise supervision is challenging in 1) how to obtain the rank list of whole training set when the batch size of the deep network is always small and 2) how to utilize the listwise supervision. In this paper, we present a novel deep policy hashing architecture with two systems are learned in parallel: aquery network and a shared and slowly changingdatabase network. The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e.g., mean average precision (MAP), and 3) the database network is updated as the query network. Extensive evaluations on several benchmark datasets show that the proposed method brings substantial improvements over state-of-the-art hashing methods.
References
[1]
Zhangjie Cao, Mingsheng Long, Jianmin Wang, and S Yu Philip. 2017. HashNet: Deep Learning to Hash by Continuation. In Proceedings of the International Conference on Computer Vision. 5609--5618.
[2]
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 ACM International Conference on Image and Video Retrieval. Article 48, bibinfonumpages9 pages.
[3]
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.
[4]
Aristides Gionis, Piotr Indyk, and Rajeev Motwani. 1999. Similarity Search in High Dimensions via Hashing. In Proceedings of the 25th International Conference on Very Large Data Bases. 518--529.
[5]
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 Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 12 (2013), 2916--2929.
[6]
Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, and Tieniu Tan. 2018. Fast supervised discrete hashing. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, 2 (2018), 490--496.
[7]
Mark J. Huiskes and Michael S. Lew. 2008. The MIR Flickr Retrieval Evaluation. In Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval (MIR '08). ACM, New York, NY, USA, 39--43.
[8]
Brian Kulis and Trevor Darrell. 2009. Learning to hash with binary reconstructive embeddings. In Proceedings of the Advances in neural information processing systems. 1042--1050.
[9]
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.
[10]
Wu-Jun Li, Sheng Wang, and Wang-Cheng Kang. 2015. Feature learning based deep supervised hashing with pairwise labels. arXiv preprint arXiv:1511.03855 (2015).
[11]
Haomiao Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. 2016. Deep supervised hashing for fast image retrieval. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2064--2072.
[12]
Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. 2012. Supervised hashing with kernels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2074--2081.
[13]
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning. 1928--1937.
[14]
Mohammad Norouzi and David M Blei. 2011. Minimal loss hashing for compact binary codes. In Proceedings of the 28th international conference on machine learning. Citeseer, 353--360.
[15]
Mohammad Rastegari, Ali Farhadi, and David Forsyth. 2012. Attribute discovery via predictable discriminative binary codes. In Proceedings of the European Conference on Computer Vision. Springer, 876--889.
[16]
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et almbox. 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, Vol. 115, 3 (2015), 211--252.
[17]
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 (MM '17). ACM, New York, NY, USA, 1522--1530.
[18]
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.
[19]
Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
[20]
Richard S Sutton and Andrew G Barto. 2011. Reinforcement learning: An introduction. (2011).
[21]
Jiabao Wang, Yang Li, Xiancai Zhang, Zhuang Miao, and Gang Tao. 2017. Deep Supervised Hashing with Pairwise Bit Loss. In Proceedings of the 2017 International Conference on Deep Learning Technologies (ICDLT '17). ACM, New York, NY, USA, 70--74.
[22]
Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang. 2016. Learning to hash for indexing big data: a survey. Proc. IEEE, Vol. 104, 1 (2016), 34--57.
[23]
Jun Wang, Wei Liu, Andy X Sun, and Yu-Gang Jiang. 2013. Learning hash codes with listwise supervision. In Proceedings of the IEEE International Conference on Computer Vision. 3032--3039.
[24]
Jingdong Wang, Ting Zhang, Nicu Sebe, Heng Tao Shen, et almbox. 2017. A survey on learning to hash. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017).
[25]
Yair Weiss, Antonio Torralba, and Rob Fergus. 2009. Spectral hashing. In Advances in neural information processing systems. 1753--1760.
[26]
Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, Vol. 8, 3--4 (1992), 229--256.
[27]
Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S Davis, Kristen Grauman, and Rogerio Feris. 2018. Blockdrop: Dynamic inference paths in residual networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8817--8826.
[28]
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 Association for the Advancement of Artificial Intelligence, Vol. 1. 2.
[29]
Huei-Fang Yang, Kevin Lin, and Chu-Song Chen. 2018. Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, 2 (2018), 437--451.
[30]
Jian Zhang, Yuxin Peng, and Zhaoda Ye. 2018. Deep Reinforcement Learning for Image Hashing. arXiv preprint arXiv:1802.02904 (2018).
[31]
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 (MM '13). ACM, New York, NY, USA, 123--132.
[32]
Xi Zhang, Hanjiang Lai, and Jiashi Feng. 2018. Attention-Aware Deep Adversarial Hashing for Cross-Modal Retrieval. In Proceedings of the European Conference on Computer Vision. 614--629.
[33]
Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. 2016. Deep Hashing Network for Efficient Similarity Retrieval. In Proceedings of the Association for the Advancement of Artificial Intelligence. 2415--2421.
[34]
Bohan Zhuang, Guosheng Lin, Chunhua Shen, and Ian Reid. 2016. Fast training of triplet-based deep binary embedding networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5955--5964.
Index Terms
- Deep Policy Hashing Network with Listwise Supervision
Recommendations
Scalable Multimedia Retrieval by Deep Learning Hashing with Relative Similarity Learning
MM '15: Proceedings of the 23rd ACM international conference on MultimediaLearning-based hashing methods are becoming the mainstream for approximate scalable multimedia retrieval. They consist of two main components: hash codes learning for training data and hash functions learning for new data points. Tremendous efforts have ...
Supervised hashing with latent factor models
SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrievalDue to its low storage cost and fast query speed, hashing has been widely adopted for approximate nearest neighbor search in large-scale datasets. Traditional hashing methods try to learn the hash codes in an unsupervised way where the metric (Euclidean)...
Rank Preserving Hashing for Rapid Image Search
DCC '15: Proceedings of the 2015 Data Compression ConferenceIn recent years, hashing techniques are becoming overwhelmingly popular for their high efficiency in handling large-scale computer vision applications. It has been shown that hashing techniques which leverage supervised information can significantly ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2019
427 pages
ISBN:9781450367653
DOI:10.1145/3323873
- General Chairs:
- Abdulmotaleb El Saddik,
- Alberto Del Bimbo,
- Zhongfei Zhang,
- Program Chairs:
- Alexander Hauptmann,
- K. Selcuk Candan,
- Marco Bertini,
- Lexing Xie,
- Xiao-Yong Wei
Copyright © 2019 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
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 June 2019
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- the National Natural Science Foundation of China under Grants
- the Research Foundation of Science and Technology Plan Project in Guangdong Province
Conference
ICMR '19
Sponsor:
Acceptance Rates
Overall Acceptance Rate 254 of 830 submissions, 31%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 127Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 31 Dec 2024
Other Metrics
Citations
Cited By
View all- Yang YZhang JWang QLiu B(2020)Deep High-order Asymmetric Supervised Hashing for Image Retrieval2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9207475(1-7)Online publication date: Jul-2020
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in