NDLI: Deep supervised hashing network with integrated regularisation

Content Provider	IET Digital Library
Author	Liao, Jianxin Li, Baoran Yang, Di Wang, Jingyu Qi, Qi Wang, Jing
Abstract	Hashing has been widely deployed to approximate nearest neighbour search for large-scale multimedia retrieval tasks due to storage and retrieval efficiency. State-of-the-art supervised hashing methods for image retrieval construct deep structures to simultaneously learn image representation and generate good hash codes, and the key step among them is simultaneously learned feature representation and binary hash code. Existing methods use similarity and regularity loss to train deep hashing systems, but these two functions usually work together but not cooperative, which may lead to inadequate performance of the whole system. In this study, a new method for training deep hashing system to learn compact binary codes is presented. The deep supervised hashing network with integrated regularisation (DSHIR) system develop the zero division restriction as a new part of the loss function, which settles the problem of cooperatively guiding the system generate similarity preserving binary codes. DSHIR system also modifies the similarity handling loss to better extract features from image data, which promotes the performance compared to existing end-to-end deep hashing systems. Experiments show that DSHIR yields about 10 per cent higher mean average precision on CIFAR-10 dataset, and also promote on other evaluation indexes compared with state-of-the-art systems.
Starting Page	2143
Ending Page	2151
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6644
Journal	IET Image Processing
Publisher Date	2019-07-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximate Nearest Neighbour Search Binary Codes Binary Hash Code Code Compact Binary Codes Computer Vision And Image Processing Technique Deep Hashing System Deep Structures Deep Supervised Hashing Network DSHIR System Existing End-to-end Deep Hashing System Existing Method Feature Extraction File Organisation Good Hash Code Hashing Method Image Classification Image Recognition Image Representation Image Retrieval Indexing Information Analysis And Indexing Information Retrieval Technique Integrated Regularisation System Knowledge Engineering Technique Large-scale Multimedia Retrieval Task Learning in AI Optical, Image And Video Signal Processing Retrieval Efficiency State-of-the-art System Statistics Storage
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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