NDLI: Deep binary constraint hashing for fast image retrieval

Content Provider	IET Digital Library
Author	Li, Yang Miao, Zhuang Wang, Jiabao Zhang, Yafei
Abstract	With the development of deep hashing learning, several end-to-end deep architectures have been proposed for fast image retrieval. However, learning to hash is essentially a mixed integer nonlinear optimisation problem with the non-deterministic polynomial-time (NP)-hard nature, which makes the standard back-propagation algorithm infeasible. A novel pairwise loss function with additional binary constraint via siamese network is proposed to improve the representation ability of hash codes. Compared to previous works, we force the output of each hidden node close to −1 or +1, which can produce more compact and discriminative hash codes. Extensive experimental results demonstrate that the method can generate more favourable results than existing state-of-the-art hash function learning methods with large margins.
Starting Page	25
Ending Page	27
Page Count	3
ISSN	00135194
Volume Number	54
e-ISSN	1350911X
Issue Number	Issue 1, Jan (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/54/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2017.3620
Journal	Electronics Letters
Publisher Date	2017-11-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Backpropagation Computational Complexity Computer Vision And Image Processing Technique Deep Binary Constraint Hashing Deep Hashing Learning End-to-end Deep Architectures Fast Image Retrieval Hash Code Hash Function Learning Method Image Retrieval Information Retrieval Technique Integer Programming Knowledge Engineering Technique Mixed Integer Nonlinear Optimisation Problem Neural Computing Technique Nonlinear Programming NP-hard Nature Optimisation Technique Pairwise Loss Function Representation Ability Siamese Network Standard Backpropagation Algorithm
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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