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|a 10.1109/TIP.2020.3040536
|2 doi
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|a eng
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| 100 |
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|a Shi, Xiaoshuang
|e verfasserin
|4 aut
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| 245 |
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|a A Scalable Optimization Mechanism for Pairwise Based Discrete Hashing
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|c 2021
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|a Text
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|a Date Revised 16.12.2020
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Maintaining the pairwise relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simple and intuitive method is to utilize two identical code matrices produced by hash functions to approximate a pairwise real label matrix. However, the resulting quartic problem in term of hash functions is difficult to directly solve due to the non-convex and non-smooth nature of the objective. In this paper, unlike previous optimization methods using various relaxation strategies, we aim to directly solve the original quartic problem using a novel alternative optimization mechanism to linearize the quartic problem by introducing a linear regression model. Additionally, we find that gradually learning each batch of binary codes in a sequential mode, i.e. batch by batch, is greatly beneficial to the convergence of binary code learning. Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes. To further improve the smoothness, we also propose a greedy symmetric discrete hashing algorithm to update each bit of batch binary codes. Moreover, we extend the proposed optimization mechanism to solve the non-convex optimization problems for binary code learning in many other pairwise based hashing algorithms. Extensive experiments on benchmark single-label and multi-label databases demonstrate the superior performance of the proposed mechanism over recent state-of-the-art methods on two kinds of retrieval tasks: similarity and ranking order. The source codes are available on https://github.com/xsshi2015/Scalable-Pairwise-based-Discrete-Hashing
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|a Journal Article
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| 700 |
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|a Xing, Fuyong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Zizhao
|e verfasserin
|4 aut
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| 700 |
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|a Sapkota, Manish
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Zhenhua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Lin
|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 30(2021) vom: 03., Seite 1130-1142
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|x 1941-0042
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| 773 |
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|g volume:30
|g year:2021
|g day:03
|g pages:1130-1142
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|u http://dx.doi.org/10.1109/TIP.2020.3040536
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