A New Variational Method for Deep Supervised Semantic Image Hashing

Furen Zhuang, Pierre Moulin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing them to be used directly as hash bits. We show how our method allows balanced bits to be directly specified, and is superior to state-of-the-art methods across four datasets.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4532-4536
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

Keywords

  • hashing
  • image
  • retrieval
  • supervised

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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