DDSH: Deep distribution-separating hashing for image retrieval

Junjie Chen, Anran WANG*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

With the rapid growth of web images, binary hashing method has received increasing attention due to the storage efficiency and the ability for fast retrieval. Recently, deep hashing methods have achieved the state-of-the-art performance by utilizing deep neural networks in hash code learning. Most of these methods are trained with the supervision of triplet labels or pairwise relationships. In this paper, we propose a deep hashing framework called deep distribution-separating hashing (DDSH) method. The main novelty of our learning framework lies in the supervision which enforces to separate the distribution of similar pairs from the distribution of dissimilar pairs. In this way, the gap between similar pairs and dissimilar pairs is enlarged. Experimental results show that our proposed deep hashing method outperforms state-of-the-art approaches on two widely used benchmark datasets: CIFAR-10 and PASCAL VOC 2007.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
EditorsBing Zeng, Hongliang Li, Qingming Huang, Abdulmotaleb El Saddik, Shuqiang Jiang, Xiaopeng Fan
PublisherSpringer Verlag
Pages560-570
Number of pages11
ISBN (Print)9783319773827
DOIs
Publication statusPublished - 2018
Event18th Pacific-Rim Conference on Multimedia, PCM 2017 - Harbin, China
Duration: 28 Sep 201729 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10736 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Pacific-Rim Conference on Multimedia, PCM 2017
Country/TerritoryChina
CityHarbin
Period28/09/1729/09/17

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Deep hashing
  • Hash code learning
  • Image retrieval

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