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Image Quality Assessment Using Similar Scene as Reference Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Learning to Hash with Binary Deep Neural Network

Authors : Thanh-Toan Do, Anh-Dzung Doan, Ngai-Man Cheung

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

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Abstract

This work proposes deep network models and learning algorithms for unsupervised and supervised binary hashing. Our novel network design constrains one hidden layer to directly output the binary codes. This addresses a challenging issue in some previous works: optimizing non-smooth objective functions due to binarization. Moreover, we incorporate independence and balance properties in the direct and strict forms in the learning. Furthermore, we include similarity preserving property in our objective function. Our resulting optimization with these binary, independence, and balance constraints is difficult to solve. We propose to attack it with alternating optimization and careful relaxation. Experimental results on three benchmark datasets show that our proposed methods compare favorably with the state of the art.

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previous chapter Minimal Solvers for Generalized Pose and Scale Estimation from Two Rays and One Point
next chapter Automatically Selecting Inference Algorithms for Discrete Energy Minimisation
Footnotes
1
Alternatively, we can constrain the independence and balance on \({\mathbf B}\). This, however, makes the optimization very difficult.
 
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Metadata
Title
Learning to Hash with Binary Deep Neural Network
Authors
Thanh-Toan Do
Anh-Dzung Doan
Ngai-Man Cheung
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46454-1_14

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