Data-Dependent Hashing Based on p-Stable Distribution
The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property...
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| Published in: | IEEE transactions on image processing Vol. 23; no. 12; pp. 5033 - 5046 |
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| Main Authors: | , , , , |
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| Language: | English |
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IEEE
01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
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| Abstract | The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each projection vector is a linear combination of multiple random vectors subject to p-stable distribution, in which the weights for the linear combination are learned based on the training data. An orthogonal matrix is then learned data-dependently for minimizing the thresholding error in quantization. Combining the projection method and orthogonal matrix, we develop an unsupervised hashing scheme, which preserves the Euclidean distance. Compared with data-independent hashing methods, our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes. Different from many data-dependent hashing methods, our method accommodates multiple hash tables and is not restricted by the number of hash functions. To extend our method to a supervised scenario, we incorporate a supervised label propagation scheme into the proposed projection method. This results in a supervised hashing scheme, which preserves semantic similarity of data. Experimental results show that our methods have outperformed several state-of-the-art hashing approaches in both effectiveness and efficiency. |
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| AbstractList | The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each projection vector is a linear combination of multiple random vectors subject to p-stable distribution, in which the weights for the linear combination are learned based on the training data. An orthogonal matrix is then learned data-dependently for minimizing the thresholding error in quantization. Combining the projection method and orthogonal matrix, we develop an unsupervised hashing scheme, which preserves the Euclidean distance. Compared with data-independent hashing methods, our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes. Different from many data-dependent hashing methods, our method accommodates multiple hash tables and is not restricted by the number of hash functions. To extend our method to a supervised scenario, we incorporate a supervised label propagation scheme into the proposed projection method. This results in a supervised hashing scheme, which preserves semantic similarity of data. Experimental results show that our methods have outperformed several state-of-the-art hashing approaches in both effectiveness and efficiency. The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each projection vector is a linear combination of multiple random vectors subject to p-stable distribution, in which the weights for the linear combination are learned based on the training data. An orthogonal matrix is then learned data-dependently for minimizing the thresholding error in quantization. Combining the projection method and orthogonal matrix, we develop an unsupervised hashing scheme, which preserves the Euclidean distance. Compared with data-independent hashing methods, our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes. Different from many data-dependent hashing methods, our method accommodates multiple hash tables and is not restricted by the number of hash functions. To extend our method to a supervised scenario, we incorporate a supervised label propagation scheme into the proposed projection method. This results in a supervised hashing scheme, which preserves semantic similarity of data. Experimental results show that our methods have outperformed several state-of-the-art hashing approaches in both effectiveness and efficiency.The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each projection vector is a linear combination of multiple random vectors subject to p-stable distribution, in which the weights for the linear combination are learned based on the training data. An orthogonal matrix is then learned data-dependently for minimizing the thresholding error in quantization. Combining the projection method and orthogonal matrix, we develop an unsupervised hashing scheme, which preserves the Euclidean distance. Compared with data-independent hashing methods, our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes. Different from many data-dependent hashing methods, our method accommodates multiple hash tables and is not restricted by the number of hash functions. To extend our method to a supervised scenario, we incorporate a supervised label propagation scheme into the proposed projection method. This results in a supervised hashing scheme, which preserves semantic similarity of data. Experimental results show that our methods have outperformed several state-of-the-art hashing approaches in both effectiveness and efficiency. |
| Author | Ren, Peng Yang, Haichuan Bai, Xiao Zhou, Jun Cheng, Jian |
| Author_xml | – sequence: 1 givenname: Xiao surname: Bai fullname: Bai, Xiao email: baixiao@buaa.edu.cn organization: School of Computer Science and Engineering, Beihang University, Beijing, China – sequence: 2 givenname: Haichuan surname: Yang fullname: Yang, Haichuan email: yanghaichuan@outlook.com organization: School of Computer Science and Engineering, Beihang University, Beijing, China – sequence: 3 givenname: Jun surname: Zhou fullname: Zhou, Jun email: jun.zhou@griffith.edu.au organization: School of Information and Communication Technology, Griffith University, Nathan, Australia – sequence: 4 givenname: Peng surname: Ren fullname: Ren, Peng email: renpenghit@126.com organization: College of Information and Control Engineering, China University of Petroleum, Qingdao, China – sequence: 5 givenname: Jian surname: Cheng fullname: Cheng, Jian email: jcheng@nlpr.ia.ac.cn organization: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China |
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| Cites_doi | 10.1109/CVPR.2010.5540024 10.1109/CVPR.2013.378 10.1090/mmono/065 10.1109/CVPR.2011.5995709 10.1109/SFCS.2000.892082 10.1109/TPAMI.2012.48 10.1145/361002.361007 10.1109/TPAMI.2011.103 10.1109/CVPR.2013.205 10.1023/B:VISI.0000029664.99615.94 10.1016/j.ijar.2008.11.006 10.1109/ICIP.2013.6738551 10.1162/0899766041732396 10.1145/276698.276876 10.1145/997817.997857 10.1109/TMM.2012.2199970 10.1109/CVPR.2006.68 10.1109/TPAMI.2009.151 10.1109/TPAMI.2011.219 10.1109/TPAMI.2012.193 10.1023/A:1011139631724 10.1007/978-1-4757-1904-8 10.1111/j.1469-1809.1936.tb02137.x 10.1007/BF02289451 10.1145/1835449.1835455 10.1145/2348283.2348293 10.1109/CVPR.2010.5540039 |
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| Keywords | hash retrieval Image retrieval p-stable distribution |
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| References | ref35 ref13 liu (ref9) 2011 ref34 ref15 ref36 krizhevsky (ref43) 2009 ref31 ref33 ref11 weiss (ref7) 2008 liu (ref32) 2012 ref2 belkin (ref18) 2001; 14 ref1 liu (ref42) 2013 ref39 ref16 ref19 kulis (ref8) 2009; 22 jolliffe (ref24) 1986; 487 fan (ref45) 2008; 9 weiss (ref21) 2012 kong (ref26) 2012 raginsky (ref17) 2009; 22 ref23 ref25 weber (ref4) 1998 ref20 ref22 ref44 heo (ref38) 2012 ref28 ref27 xu (ref41) 2011 ref29 norouzi (ref10) 2011 zhu (ref14) 2002 ref3 wang (ref30) 2010 kong (ref37) 2012 ref6 ref5 ref40 zolotarev (ref12) 1986; 65 |
| References_xml | – ident: ref31 doi: 10.1109/CVPR.2010.5540024 – ident: ref39 doi: 10.1109/CVPR.2013.378 – volume: 65 year: 1986 ident: ref12 publication-title: One-Dimensional Stable Distributions doi: 10.1090/mmono/065 – start-page: 1753 year: 2008 ident: ref7 article-title: Spectral hashing publication-title: Proc Neural Inf Process Syst Conf – start-page: 1127 year: 2010 ident: ref30 article-title: Sequential projection learning for hashing with compact codes publication-title: Proc Int Conf Mach Learn – ident: ref35 doi: 10.1109/CVPR.2011.5995709 – ident: ref16 doi: 10.1109/SFCS.2000.892082 – start-page: 634 year: 2012 ident: ref37 article-title: Double-bit quantization for hashing publication-title: Proc AAAI Conf Artif Intell – ident: ref29 doi: 10.1109/TPAMI.2012.48 – ident: ref1 doi: 10.1145/361002.361007 – year: 2002 ident: ref14 article-title: Learning from labeled and unlabeled data with label propagation – ident: ref27 doi: 10.1109/TPAMI.2011.103 – volume: 22 start-page: 1042 year: 2009 ident: ref8 article-title: Learning to hash with binary reconstructive embeddings publication-title: Proc Neural Inf Process Syst Conf – start-page: 626 year: 2013 ident: ref42 article-title: Reciprocal hash tables for nearest neighbor search publication-title: Proc 27th AAAI Conf Artif Intell – ident: ref23 doi: 10.1109/CVPR.2013.205 – ident: ref44 doi: 10.1023/B:VISI.0000029664.99615.94 – ident: ref34 doi: 10.1016/j.ijar.2008.11.006 – start-page: 340 year: 2012 ident: ref21 article-title: Multidimensional spectral hashing publication-title: Proc 12th Eur Conf Comput Vis – start-page: 2074 year: 2012 ident: ref32 article-title: Supervised hashing with kernels publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – start-page: 194 year: 1998 ident: ref4 article-title: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces publication-title: Proc Int Conf Very Large Data Bases – ident: ref15 doi: 10.1109/ICIP.2013.6738551 – ident: ref19 doi: 10.1162/0899766041732396 – ident: ref5 doi: 10.1145/276698.276876 – ident: ref6 doi: 10.1145/997817.997857 – start-page: 353 year: 2011 ident: ref10 article-title: Minimal loss hashing for compact binary codes publication-title: Proc Int Conf Mach Learn – ident: ref22 doi: 10.1109/TMM.2012.2199970 – ident: ref2 doi: 10.1109/CVPR.2006.68 – ident: ref33 doi: 10.1109/TPAMI.2009.151 – ident: ref11 doi: 10.1109/TPAMI.2011.219 – start-page: 1 year: 2011 ident: ref9 article-title: Hashing with graphs publication-title: Proc Int Conf Mach Learn – volume: 9 start-page: 1871 year: 2008 ident: ref45 article-title: Liblinear: A library for large linear classification publication-title: J Mach Learn Res – start-page: 1655 year: 2012 ident: ref26 article-title: Isotropic hashing publication-title: Proc Neural Inf Process Syst Conf – year: 2009 ident: ref43 article-title: Learning multiple layers of features from tiny images – ident: ref13 doi: 10.1109/TPAMI.2012.193 – ident: ref3 doi: 10.1023/A:1011139631724 – volume: 487 year: 1986 ident: ref24 publication-title: Principal Component Analysis doi: 10.1007/978-1-4757-1904-8 – start-page: 1631 year: 2011 ident: ref41 article-title: Complementary hashing for approximate nearest neighbor search publication-title: Proc Int Conf Comput Vis – ident: ref28 doi: 10.1111/j.1469-1809.1936.tb02137.x – volume: 14 start-page: 585 year: 2001 ident: ref18 article-title: Laplacian eigenmaps and spectral techniques for embedding and clustering publication-title: Proc Neural Inf Process Syst Conf – start-page: 2957 year: 2012 ident: ref38 article-title: Spherical hashing publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – ident: ref40 doi: 10.1007/BF02289451 – volume: 22 start-page: 1509 year: 2009 ident: ref17 article-title: Locality-sensitive binary codes from shift-invariant kernels publication-title: Proc Neural Inf Process Syst Conf – ident: ref20 doi: 10.1145/1835449.1835455 – ident: ref36 doi: 10.1145/2348283.2348293 – ident: ref25 doi: 10.1109/CVPR.2010.5540039 |
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| Snippet | The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on... The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform Data-Dependent Hashing Based on... |
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| SubjectTerms | Binary codes Educational institutions Euclidean distance Hash based algorithms Image processing Mathematical analysis Methods Preserves Projection Quantization (signal) Semantics Similarity Training Vectors Vectors (mathematics) |
| Title | Data-Dependent Hashing Based on p-Stable Distribution |
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