Locality sensitive hashing with bit selection
Locality sensitive hashing (LSH), one of the most popular hashing techniques, has attracted considerable attention for nearest neighbor search in the field of image retrieval. It can achieve promising performance only if the number of the generated hash bits is large enough. However, more hash bits...
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Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 13; pp. 14724 - 14738 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.10.2022
Springer Nature B.V |
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Abstract | Locality sensitive hashing (LSH), one of the most popular hashing techniques, has attracted considerable attention for nearest neighbor search in the field of image retrieval. It can achieve promising performance only if the number of the generated hash bits is large enough. However, more hash bits assembled to the binary codes contain massive redundant information and require more time cost and storage spaces. To alleviate this limitation, we propose a novel bit selection framework to pick important bits out of the hash bits generated by hashing techniques. Within the bit selection framework, we further exploit eleven evaluation criteria to measure the importance and similarity of each bit generated by LSH, so that the bits with high importance and less similarity are selected to assemble new binary codes. To demonstrate the effectiveness of the proposed framework of bit selection, we evaluated the proposed framework with the evaluation criteria on five commonly used data sets. Experimental results show the proposed bit selection framework works effectively in different cases, and the performance of LSH has not been degraded significantly after redundant hash bits reduced by the evaluation criteria. |
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AbstractList | Locality sensitive hashing (LSH), one of the most popular hashing techniques, has attracted considerable attention for nearest neighbor search in the field of image retrieval. It can achieve promising performance only if the number of the generated hash bits is large enough. However, more hash bits assembled to the binary codes contain massive redundant information and require more time cost and storage spaces. To alleviate this limitation, we propose a novel bit selection framework to pick important bits out of the hash bits generated by hashing techniques. Within the bit selection framework, we further exploit eleven evaluation criteria to measure the importance and similarity of each bit generated by LSH, so that the bits with high importance and less similarity are selected to assemble new binary codes. To demonstrate the effectiveness of the proposed framework of bit selection, we evaluated the proposed framework with the evaluation criteria on five commonly used data sets. Experimental results show the proposed bit selection framework works effectively in different cases, and the performance of LSH has not been degraded significantly after redundant hash bits reduced by the evaluation criteria. |
Author | Liu, Huawen Lou, Jungang Chen, Xin Zhou, Wenhua |
Author_xml | – sequence: 1 givenname: Wenhua surname: Zhou fullname: Zhou, Wenhua organization: Department of Computer Science, Zhejiang Normal University – sequence: 2 givenname: Huawen orcidid: 0000-0003-0535-4652 surname: Liu fullname: Liu, Huawen email: huaw.liu@gmail.com organization: Department of Computer Science, Shaoxing University – sequence: 3 givenname: Jungang surname: Lou fullname: Lou, Jungang organization: School of Information Engineering, Huzhou University – sequence: 4 givenname: Xin surname: Chen fullname: Chen, Xin email: xinchen@zjnu.cn organization: Department of Computer Science, Zhejiang Normal University |
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Cites_doi | 10.1016/j.patcog.2008.10.028 10.1109/TPAMI.2017.2678475 10.1109/TPAMI.2012.193 10.1007/s10489-018-1174-6 10.1109/TNNLS.2019.2954856 10.1016/j.neucom.2019.01.040 10.1109/TKDE.2020.2970050 10.1007/s10489-018-01404-1 10.1007/s10489-020-01841-x 10.1007/s10489-020-01797-y 10.1109/TIP.2020.2970577 10.1109/TIP.2017.2695895 10.1109/TMM.2017.2683258 10.1109/TKDE.2019.2913383 10.1145/2457465.2457469 10.1145/3447684 10.1145/361002.361007 10.1109/TPAMI.2011.235 10.1109/TIP.2020.3014727 10.1109/TKDE.2019.2953897 10.1109/TPAMI.2017.2699960 10.1109/TNNLS.2017.2673241 10.1109/TIP.2014.2332764 10.1007/978-3-030-55130-8_36 10.1109/ICICT4SD50815.2021.9396917 10.1109/CVPR.2008.4587638 10.1145/1835449.1835455 10.1109/CVPR.2014.272 10.1109/TNNLS.2021.3109898 10.1145/3009967 10.1145/2348283.2348293 10.1109/ICCV.2009.5459466 |
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Keywords | Nearest neighbor search Locality sensitive hashing Hash bit Bit selection Binary code |
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References | Liu, Li, Liu, Su, Zhang (CR3) 2021; 15 Zhang, Li, Zong, Zhu, Wang (CR4) 2018; 29 Wang, Zhang, Song, Sebe, Shen (CR1) 2018; 40 Chi, Zhu (CR14) 2017; 50 CR18 Gui, Liu, Sun, Tao, Tan (CR34) 2018; 40 Cai (CR11) 2021; 33 Zhu, Huang, Cheng, Cui, Shen (CR26) 2013; 31 Hoang, Do, Nguyen, Cheung (CR17) 2020; 29 Quynh, Thuy, Van, Van, Tao (CR5) 2018; 48 CR13 CR12 Liu, Liu, Le, Lee, Sun, Li (CR28) 2017; 19 Liu, Nie, Zhou, Nie, Yin (CR32) 2020; 29 CR33 CR31 Liu, He, Chang (CR27) 2017; 26 Zhang, Qian (CR2) 2021; 51 Jégou, Perronnin, Douze, Sánchez, Pérez, Schmid (CR15) 2012; 34 Gong, Lazebnik, Gordo, Perronnin (CR16) 2013; 35 CR6 Pan, You, Liu, Zhang (CR23) 2021; 51 CR8 Liu, Li, Zhang, Tian (CR10) 2020; 31 Xie, Chen, Qian (CR22) 2019; 49 Liu, Sun, Liu, Zhang (CR24) 2009; 42 CR9 CR25 CR21 Nie, Jing, Cui, Zhang, Zhu, Yin (CR29) 2020; 32 CR20 Yan, Bai, Wang, Zhou, Hancock (CR35) 2019; 337 Zhu, Zhang, Huang (CR19) 2014; 23 Shen, Liu, Yang, Xu, Huang, Shen, Hong (CR30) 2021; 33 Bentley (CR7) 1975; 18 X Zhu (3546_CR26) 2013; 31 X Zhu (3546_CR19) 2014; 23 X Liu (3546_CR27) 2017; 26 X Nie (3546_CR29) 2020; 32 HT Shen (3546_CR30) 2021; 33 3546_CR13 H Liu (3546_CR3) 2021; 15 B Zhang (3546_CR2) 2021; 51 S Zhang (3546_CR4) 2018; 29 3546_CR31 3546_CR33 3546_CR12 H Pan (3546_CR23) 2021; 51 JL Bentley (3546_CR7) 1975; 18 H Liu (3546_CR10) 2020; 31 L Chi (3546_CR14) 2017; 50 3546_CR18 C Yan (3546_CR35) 2019; 337 3546_CR9 X Xie (3546_CR22) 2019; 49 3546_CR8 3546_CR25 3546_CR6 NH Quynh (3546_CR5) 2018; 48 3546_CR20 T Hoang (3546_CR17) 2020; 29 3546_CR21 H Jégou (3546_CR15) 2012; 34 Y Gong (3546_CR16) 2013; 35 J Gui (3546_CR34) 2018; 40 X Liu (3546_CR32) 2020; 29 H Liu (3546_CR24) 2009; 42 H Liu (3546_CR28) 2017; 19 J Wang (3546_CR1) 2018; 40 D Cai (3546_CR11) 2021; 33 |
References_xml | – ident: CR18 – volume: 42 start-page: 1330 issue: 7 year: 2009 end-page: 1339 ident: CR24 article-title: Feature selection with dynamic mutual information publication-title: Pattern Recogn doi: 10.1016/j.patcog.2008.10.028 contributor: fullname: Zhang – volume: 40 start-page: 490 issue: 2 year: 2018 end-page: 496 ident: CR34 article-title: Fast supervised fiscrete hashing publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2678475 contributor: fullname: Tan – volume: 50 start-page: 1 issue: 1 year: 2017 end-page: 36 ident: CR14 article-title: Hashing techniques: A survey and taxonomy publication-title: ACM Computting Surveys contributor: fullname: Zhu – volume: 35 start-page: 2916 issue: 12 year: 2013 end-page: 2929 ident: CR16 article-title: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2012.193 contributor: fullname: Perronnin – ident: CR12 – volume: 48 start-page: 3807 issue: 10 year: 2018 end-page: 3826 ident: CR5 article-title: An efficient image retrieval method using adaptive weights publication-title: Appl Intell doi: 10.1007/s10489-018-1174-6 contributor: fullname: Tao – volume: 31 start-page: 4318 issue: 10 year: 2020 end-page: 4329 ident: CR10 article-title: Adaptive hashing with sparse matrix factorization publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2019.2954856 contributor: fullname: Tian – volume: 337 start-page: 58 year: 2019 end-page: 66 ident: CR35 article-title: Cross-modal hashing with semantic deep embedding publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.01.040 contributor: fullname: Hancock – ident: CR33 – volume: 33 start-page: 3351 issue: 10 year: 2021 end-page: 3365 ident: CR30 article-title: Exploiting subspace relation in semantic labels for cross-modal hashing publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2020.2970050 contributor: fullname: Hong – volume: 49 start-page: 2391 issue: 6 year: 2019 end-page: 2399 ident: CR22 article-title: Twin maximum entropy discriminations for classification publication-title: Appl Intell doi: 10.1007/s10489-018-01404-1 contributor: fullname: Qian – ident: CR6 – volume: 51 start-page: 752 issue: 2 year: 2021 end-page: 774 ident: CR23 article-title: Pearson correlation coefficient-based pheromone refactoring mechanism for multi-colony ant colony optimization publication-title: Appl Intell doi: 10.1007/s10489-020-01841-x contributor: fullname: Zhang – ident: CR8 – volume: 51 start-page: 493 issue: 1 year: 2021 end-page: 505 ident: CR2 article-title: Autoencoder-based unsupervised clustering and hashing publication-title: Appl Intell doi: 10.1007/s10489-020-01797-y contributor: fullname: Qian – ident: CR25 – volume: 29 start-page: 4254 year: 2020 end-page: 4268 ident: CR32 article-title: Model optimization boosting framework for linear model hash learning publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2020.2970577 contributor: fullname: Yin – volume: 26 start-page: 5367 issue: 11 year: 2017 end-page: 5380 ident: CR27 article-title: Hash bit selection for nearest neighbor search publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2017.2695895 contributor: fullname: Chang – volume: 19 start-page: 1848 issue: 8 year: 2017 end-page: 1859 ident: CR28 article-title: Nonparametric sparse matrix decomposition for cross-view dimensionality reduction publication-title: IEEE Transactions on Multimedia doi: 10.1109/TMM.2017.2683258 contributor: fullname: Li – volume: 32 start-page: 1951 issue: 10 year: 2020 end-page: 1965 ident: CR29 article-title: Joint multi-view hashing for large-scale near-duplicate video retrieval publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2019.2913383 contributor: fullname: Yin – ident: CR21 – volume: 31 start-page: 1 issue: 2 year: 2013 end-page: 24 ident: CR26 article-title: Sparse hashing for fast multimedia search publication-title: ACM Trans Inf Syst doi: 10.1145/2457465.2457469 contributor: fullname: Shen – volume: 15 start-page: 91:1 issue: 5 year: 2021 end-page: 91: ident: CR3 article-title: Anomaly detection with kernel preserving embedding publication-title: ACM Transactions on Knowledge Discovery from Data doi: 10.1145/3447684 contributor: fullname: Zhang – volume: 18 start-page: 509 issue: 9 year: 1975 end-page: 517 ident: CR7 article-title: Multidimensional binary search trees used for associative searching publication-title: Commun ACM doi: 10.1145/361002.361007 contributor: fullname: Bentley – volume: 34 start-page: 1704 issue: 9 year: 2012 end-page: 1716 ident: CR15 article-title: Aggregating local image descriptors into compact codes publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2011.235 contributor: fullname: Schmid – volume: 29 start-page: 8391 year: 2020 end-page: 8406 ident: CR17 article-title: Unsupervised deep cross-modality spectral hashing publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2020.3014727 contributor: fullname: Cheung – ident: CR31 – ident: CR13 – ident: CR9 – volume: 33 start-page: 2337 issue: 6 year: 2021 end-page: 2348 ident: CR11 article-title: A revisit of hashing algorithms for approximate nearest neighbor search publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2019.2953897 contributor: fullname: Cai – volume: 40 start-page: 769 issue: 4 year: 2018 end-page: 790 ident: CR1 article-title: A survey on learning to hash publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2699960 contributor: fullname: Shen – volume: 29 start-page: 1774 issue: 5 year: 2018 end-page: 1785 ident: CR4 article-title: Efficient knn classification with different numbers of nearest neighbors publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2017.2673241 contributor: fullname: Wang – volume: 23 start-page: 3737 issue: 9 year: 2014 end-page: 3750 ident: CR19 article-title: A sparse embedding and least variance encoding approach to hashing publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2014.2332764 contributor: fullname: Huang – ident: CR20 – volume: 15 start-page: 91:1 issue: 5 year: 2021 ident: 3546_CR3 publication-title: ACM Transactions on Knowledge Discovery from Data doi: 10.1145/3447684 contributor: fullname: H Liu – ident: 3546_CR21 doi: 10.1007/978-3-030-55130-8_36 – volume: 337 start-page: 58 year: 2019 ident: 3546_CR35 publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.01.040 contributor: fullname: C Yan – volume: 42 start-page: 1330 issue: 7 year: 2009 ident: 3546_CR24 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2008.10.028 contributor: fullname: H Liu – volume: 23 start-page: 3737 issue: 9 year: 2014 ident: 3546_CR19 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2014.2332764 contributor: fullname: X Zhu – ident: 3546_CR33 doi: 10.1109/ICICT4SD50815.2021.9396917 – ident: 3546_CR8 – ident: 3546_CR13 – volume: 29 start-page: 4254 year: 2020 ident: 3546_CR32 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2020.2970577 contributor: fullname: X Liu – volume: 40 start-page: 769 issue: 4 year: 2018 ident: 3546_CR1 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2699960 contributor: fullname: J Wang – ident: 3546_CR6 doi: 10.1109/CVPR.2008.4587638 – volume: 31 start-page: 4318 issue: 10 year: 2020 ident: 3546_CR10 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2019.2954856 contributor: fullname: H Liu – volume: 33 start-page: 2337 issue: 6 year: 2021 ident: 3546_CR11 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2019.2953897 contributor: fullname: D Cai – ident: 3546_CR25 doi: 10.1145/1835449.1835455 – ident: 3546_CR20 doi: 10.1109/CVPR.2014.272 – volume: 18 start-page: 509 issue: 9 year: 1975 ident: 3546_CR7 publication-title: Commun ACM doi: 10.1145/361002.361007 contributor: fullname: JL Bentley – ident: 3546_CR9 doi: 10.1109/TNNLS.2021.3109898 – volume: 49 start-page: 2391 issue: 6 year: 2019 ident: 3546_CR22 publication-title: Appl Intell doi: 10.1007/s10489-018-01404-1 contributor: fullname: X Xie – volume: 51 start-page: 752 issue: 2 year: 2021 ident: 3546_CR23 publication-title: Appl Intell doi: 10.1007/s10489-020-01841-x contributor: fullname: H Pan – volume: 33 start-page: 3351 issue: 10 year: 2021 ident: 3546_CR30 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2020.2970050 contributor: fullname: HT Shen – ident: 3546_CR18 – volume: 31 start-page: 1 issue: 2 year: 2013 ident: 3546_CR26 publication-title: ACM Trans Inf Syst doi: 10.1145/2457465.2457469 contributor: fullname: X Zhu – volume: 34 start-page: 1704 issue: 9 year: 2012 ident: 3546_CR15 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2011.235 contributor: fullname: H Jégou – volume: 26 start-page: 5367 issue: 11 year: 2017 ident: 3546_CR27 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2017.2695895 contributor: fullname: X Liu – volume: 19 start-page: 1848 issue: 8 year: 2017 ident: 3546_CR28 publication-title: IEEE Transactions on Multimedia doi: 10.1109/TMM.2017.2683258 contributor: fullname: H Liu – volume: 35 start-page: 2916 issue: 12 year: 2013 ident: 3546_CR16 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2012.193 contributor: fullname: Y Gong – volume: 48 start-page: 3807 issue: 10 year: 2018 ident: 3546_CR5 publication-title: Appl Intell doi: 10.1007/s10489-018-1174-6 contributor: fullname: NH Quynh – volume: 51 start-page: 493 issue: 1 year: 2021 ident: 3546_CR2 publication-title: Appl Intell doi: 10.1007/s10489-020-01797-y contributor: fullname: B Zhang – volume: 50 start-page: 1 issue: 1 year: 2017 ident: 3546_CR14 publication-title: ACM Computting Surveys doi: 10.1145/3009967 contributor: fullname: L Chi – ident: 3546_CR31 doi: 10.1145/2348283.2348293 – volume: 40 start-page: 490 issue: 2 year: 2018 ident: 3546_CR34 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2678475 contributor: fullname: J Gui – ident: 3546_CR12 doi: 10.1109/ICCV.2009.5459466 – volume: 32 start-page: 1951 issue: 10 year: 2020 ident: 3546_CR29 publication-title: IEEE Trans Knowl Data Eng doi: 10.1109/TKDE.2019.2913383 contributor: fullname: X Nie – volume: 29 start-page: 1774 issue: 5 year: 2018 ident: 3546_CR4 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2017.2673241 contributor: fullname: S Zhang – volume: 29 start-page: 8391 year: 2020 ident: 3546_CR17 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2020.3014727 contributor: fullname: T Hoang |
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Snippet | Locality sensitive hashing (LSH), one of the most popular hashing techniques, has attracted considerable attention for nearest neighbor search in the field of... |
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SubjectTerms | Artificial Intelligence Binary codes Computer Science Criteria Image retrieval Machines Manufacturing Mechanical Engineering Processes Similarity Special Issue on Multi-view Learning |
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Title | Locality sensitive hashing with bit selection |
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