Learning to hash with binary deep neural network

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

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134 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2016
Subtitle of host publication14th European Conference Amsterdam, The Netherlands, October 11–14, 2016 Proceedings, Part V
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Place of PublicationCham Switzerland
Number of pages16
ISBN (Electronic)9783319464541
ISBN (Print)9783319464534
Publication statusPublished - 2016
Externally publishedYes
EventEuropean Conference on Computer Vision 2016 - Amsterdam, Netherlands
Duration: 11 Oct 201614 Oct 2016
Conference number: 14th
https://link.springer.com/book/10.1007/978-3-319-46448-0 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Computer Vision 2016
Abbreviated titleECCV 2016
Internet address


  • Discrete optimizatization
  • Learning to hash
  • Neural network

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