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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2014.2352458
|2 doi
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|a eng
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|a Bai, Xiao
|e verfasserin
|4 aut
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|a Data-dependent hashing based on p-stable distribution
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|c 2014
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 30.03.2015
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|a Date Revised 28.10.2014
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
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|a Yang, Haichuan
|e verfasserin
|4 aut
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| 700 |
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|a Zhou, Jun
|e verfasserin
|4 aut
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| 700 |
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|a Ren, Peng
|e verfasserin
|4 aut
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| 700 |
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|a Cheng, Jian
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 12 vom: 28. Dez., Seite 5033-46
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|x 1941-0042
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|g volume:23
|g year:2014
|g number:12
|g day:28
|g month:12
|g pages:5033-46
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|u http://dx.doi.org/10.1109/TIP.2014.2352458
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