Deep Fuzzy Hashing Network for Efficient Image Retrieval

Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming space with similarity well preserved. The traditional hashing methods usually represent image content by hand-crafted features. Deep hashing met...

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Vydáno v:	IEEE transactions on fuzzy systems Ročník 29; číslo 1; s. 166 - 176
Hlavní autoři:	Lu, Huimin, Zhang, Ming, Xu, Xing, Li, Yujie, Shen, Heng Tao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Binary codes Data models Data structures Deep neural network (DNN) Feature extraction Fuzzy logic fuzzy neural net-work (FNN) Fuzzy neural networks hashing learning Image management Image retrieval Machine learning Neural networks Similarity
ISSN:	1063-6706, 1941-0034
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Abstract	Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming space with similarity well preserved. The traditional hashing methods usually represent image content by hand-crafted features. Deep hashing methods based on deep neural network (DNN) architectures can generate more effective image features and obtain better retrieval performance. However, the underlying data structure is hardly captured by existing DNN models. Moreover, the similarity (either visually or semantically) between pairwise images is ambiguous, even uncertain, to be measured in the existing deep hashing methods. In this article, we propose a novel hashing method termed deep fuzzy hashing network (DFHN) to overcome the shortcomings of existing deep hashing approaches. Our DFHN method combines the fuzzy logic technique and the DNN to learn more effective binary codes, which can leverage fuzzy rules to model the uncertainties underlying the data. Derived from fuzzy logic theory, the generalized hamming distance is devised in the convolutional layers and fully connected layers in our DFHN to model their outputs, which come from an efficient xor operation on given inputs and weights. Extensive experiments show that our DFHN method obtains competitive retrieval accuracy with highly efficient training speed compared with several state-of-the-art deep hashing approaches on two large-scale image datasets: CIFAR-10 and NUS-WIDE.
AbstractList	Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming space with similarity well preserved. The traditional hashing methods usually represent image content by hand-crafted features. Deep hashing methods based on deep neural network (DNN) architectures can generate more effective image features and obtain better retrieval performance. However, the underlying data structure is hardly captured by existing DNN models. Moreover, the similarity (either visually or semantically) between pairwise images is ambiguous, even uncertain, to be measured in the existing deep hashing methods. In this article, we propose a novel hashing method termed deep fuzzy hashing network (DFHN) to overcome the shortcomings of existing deep hashing approaches. Our DFHN method combines the fuzzy logic technique and the DNN to learn more effective binary codes, which can leverage fuzzy rules to model the uncertainties underlying the data. Derived from fuzzy logic theory, the generalized hamming distance is devised in the convolutional layers and fully connected layers in our DFHN to model their outputs, which come from an efficient xor operation on given inputs and weights. Extensive experiments show that our DFHN method obtains competitive retrieval accuracy with highly efficient training speed compared with several state-of-the-art deep hashing approaches on two large-scale image datasets: CIFAR-10 and NUS-WIDE.
Author	Xu, Xing Lu, Huimin Zhang, Ming Li, Yujie Shen, Heng Tao
Author_xml	– sequence: 1 givenname: Huimin orcidid: 0000-0001-9794-3221 surname: Lu fullname: Lu, Huimin email: dr.huimin.lu@ieee.org organization: Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan – sequence: 2 givenname: Ming orcidid: 0000-0002-9732-4460 surname: Zhang fullname: Zhang, Ming email: zmingcs@gmail.com organization: Center for Future Multimedia and the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Xing orcidid: 0000-0001-5685-3123 surname: Xu fullname: Xu, Xing email: xing.xu@uestc.edu.cn organization: Center for Future Multimedia and the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Yujie orcidid: 0000-0002-0275-2797 surname: Li fullname: Li, Yujie email: yzyjli@gmail.com organization: School of Information Engineering, Yangzhou University, Yangzhou, China – sequence: 5 givenname: Heng Tao orcidid: 0000-0002-2999-2088 surname: Shen fullname: Shen, Heng Tao email: shenhengtao@hotmail.com organization: Center for Future Multimedia and the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
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Cites_doi	10.1109/TKDE.2020.2970050 10.1109/CVPRW.2015.7301269 10.1109/12.106218 10.1109/ICCV.2015.123 10.1109/ICCV.2009.5459469 10.1109/TIP.2017.2676345 10.1109/TIP.2015.2405340 10.1109/TPAMI.2012.48 10.1007/978-3-319-19683-1_31 10.1109/CVPR.2014.275 10.1109/CVPR.2015.7298947 10.1109/TPAMI.2018.2789887 10.1007/s10462-018-9630-6 10.1109/34.895972 10.1109/TPAMI.2012.193 10.1109/TCSVT.2017.2771332 10.1109/TCYB.2018.2883970 10.1109/TFUZZ.2016.2574915 10.1109/ICCCYB.2005.1511558 10.1109/TNNLS.2014.2346537 10.1109/CVPR.2017.243 10.1109/TIP.2015.2467315 10.1109/CVPR.2014.81 10.1109/91.298447 10.1109/CVPR.2016.90 10.1016/0165-0114(94)90279-8 10.1109/TIP.2016.2612883 10.1109/91.963761 10.1109/CVPR.2016.227 10.1016/S0031-3203(01)00162-5 10.1016/j.entcs.2009.07.045 10.1145/1646396.1646452 10.1023/B:VISI.0000029664.99615.94 10.1109/CVPRW.2014.131 10.1109/CVPR.2009.5206848 10.1109/TNNLS.2020.2967597 10.1109/TCYB.2019.2928180 10.1016/j.neucom.2015.11.133 10.1007/978-3-642-35221-8 10.1109/CVPR.2015.7298594 10.1109/TPAMI.2019.2914897 10.1016/j.patcog.2017.02.034
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References	ref13 ref56 ref12 ref59 ref14 ref53 ref52 babenk (ref39) 1994; 61 ref55 ref11 ref54 bay (ref2) 0 zhao (ref27) 0 ref17 kumar (ref46) 2019; 38 khan (ref32) 2019 szegedy (ref15) 0 ref51 ref50 zhu (ref57) 0 weiss (ref49) 0 ref48 ref47 ref42 ref41 gionis (ref18) 0 ref44 ref43 ronneberger (ref16) 0 ref8 ref7 ref9 fan (ref31) 0 ref4 ref3 ref6 ref5 ref40 krizhevsky (ref58) 2009 ref34 liu (ref19) 0 ref37 ref36 ref30 ref33 kulis (ref60) 0 ref1 lin (ref45) 2015; 26 ref24 ref23 norouzi (ref20) 0 ref26 ref22 ref21 xia (ref25) 0 babenko (ref38) 0 ref28 krizhevsky (ref10) 0 simonyan (ref35) 0 ref29
References_xml	– start-page: 1106 year: 0 ident: ref10 article-title: ImagNet classification with deep convolutional neural networks publication-title: Proc Annu Conf Neural Inf Process Syst – start-page: 404 year: 0 ident: ref2 article-title: SURF: Speeded up robust features publication-title: Proc Eur Conf Comput Vis – ident: ref21 doi: 10.1109/TKDE.2020.2970050 – ident: ref22 doi: 10.1109/CVPRW.2015.7301269 – ident: ref29 doi: 10.1109/12.106218 – ident: ref14 doi: 10.1109/ICCV.2015.123 – ident: ref33 doi: 10.1109/ICCV.2009.5459469 – ident: ref54 doi: 10.1109/TIP.2017.2676345 – ident: ref53 doi: 10.1109/TIP.2015.2405340 – start-page: 1753 year: 0 ident: ref49 article-title: Spectral hashing publication-title: Proc Int Conf Neural Inf Process – ident: ref55 doi: 10.1109/TPAMI.2012.48 – ident: ref40 doi: 10.1007/978-3-319-19683-1_31 – ident: ref50 doi: 10.1109/CVPR.2014.275 – start-page: 1556 year: 0 ident: ref27 article-title: Deep semantic ranking based hashing for multi-label image retrieval publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – ident: ref26 doi: 10.1109/CVPR.2015.7298947 – ident: ref51 doi: 10.1109/TPAMI.2018.2789887 – start-page: 234 year: 0 ident: ref16 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput Comput -Assisted Intervention – ident: ref30 doi: 10.1007/s10462-018-9630-6 – ident: ref6 doi: 10.1109/34.895972 – ident: ref7 doi: 10.1109/TPAMI.2012.193 – volume: 38 start-page: 2561 year: 2019 ident: ref46 article-title: Two-stage data encryption using chaotic neural networks publication-title: J Intell Fuzzy Syst – ident: ref56 doi: 10.1109/TCSVT.2017.2771332 – ident: ref23 doi: 10.1109/TCYB.2018.2883970 – ident: ref43 doi: 10.1109/TFUZZ.2016.2574915 – ident: ref47 doi: 10.1109/ICCCYB.2005.1511558 – volume: 26 start-page: 1442 year: 2015 ident: ref45 article-title: An interval type-2 neural fuzzy system for online system identification and feature elimination publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2014.2346537 – ident: ref37 doi: 10.1109/CVPR.2017.243 – ident: ref28 doi: 10.1109/TIP.2015.2467315 – start-page: 2553 year: 0 ident: ref15 article-title: Deep neural networks for object detection publication-title: Proc Annu Conf Neural Inf Process Syst – ident: ref12 doi: 10.1109/CVPR.2014.81 – ident: ref42 doi: 10.1109/91.298447 – ident: ref36 doi: 10.1109/CVPR.2016.90 – volume: 61 start-page: 1 year: 1994 ident: ref39 article-title: Invited review on the principles of fuzzy neural networks publication-title: Fuzzy Sets Syst doi: 10.1016/0165-0114(94)90279-8 – ident: ref8 doi: 10.1109/TIP.2016.2612883 – start-page: 353 year: 0 ident: ref20 article-title: Minimal loss hashing for compact binary codes publication-title: Proc Int Conf Mach Learn – ident: ref44 doi: 10.1109/91.963761 – start-page: 2156 year: 0 ident: ref25 article-title: Supervised hashing for image retrieval via image representation learning publication-title: Proc AAAI Conf Artif Intell – start-page: 1042 year: 0 ident: ref60 article-title: Learning to hash with binary reconstructive embeddings publication-title: Proc Int Conf Neural Inf Process – ident: ref5 doi: 10.1109/CVPR.2016.227 – ident: ref3 doi: 10.1016/S0031-3203(01)00162-5 – ident: ref48 doi: 10.1016/j.entcs.2009.07.045 – ident: ref59 doi: 10.1145/1646396.1646452 – ident: ref1 doi: 10.1023/B:VISI.0000029664.99615.94 – year: 2009 ident: ref58 article-title: Learning multiple layers of features from tiny images – ident: ref11 doi: 10.1109/CVPRW.2014.131 – ident: ref34 doi: 10.1109/CVPR.2009.5206848 – start-page: 584 year: 0 ident: ref38 article-title: Neural codes for image retrieval publication-title: Proc Eur Conf Comput Vis – ident: ref17 doi: 10.1109/TNNLS.2020.2967597 – start-page: 518 year: 0 ident: ref18 article-title: Similarity search in high dimensions via hashing publication-title: Proc Int Conf Very Large Data Bases – ident: ref4 doi: 10.1109/TCYB.2019.2928180 – ident: ref9 doi: 10.1016/j.neucom.2015.11.133 – year: 2019 ident: ref32 article-title: A survey of the recent architectures of deep convolutional neural networks publication-title: arXiv 1901 06032 – ident: ref41 doi: 10.1007/978-3-642-35221-8 – start-page: 2415 year: 0 ident: ref57 article-title: Deep hashing network for efficient similarity retrieval publication-title: Proc AAAI Conf Artif Intell – start-page: 2074 year: 0 ident: ref19 article-title: Supervised hashing with kernels publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – ident: ref13 doi: 10.1109/CVPR.2015.7298594 – ident: ref24 doi: 10.1109/TPAMI.2019.2914897 – ident: ref52 doi: 10.1016/j.patcog.2017.02.034 – start-page: 309 year: 0 ident: ref35 article-title: Very deep convolutional networks for large-scale image recognition publication-title: Proc Int Conf Learn Represent – start-page: 1923 year: 0 ident: ref31 article-title: Revisit fuzzy neural network: Demystifying batch normalization and ReLU with generalized hamming network publication-title: Proc Annu Conf Neural Inf Process Syst
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Snippet	Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming...
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SubjectTerms	Artificial neural networks Binary codes Data models Data structures Deep neural network (DNN) Feature extraction Fuzzy logic fuzzy neural net-work (FNN) Fuzzy neural networks hashing learning Image management Image retrieval Machine learning Neural networks Similarity
Title	Deep Fuzzy Hashing Network for Efficient Image Retrieval
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